Let's learn about Deep Learning via these 320 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.
Curious about the deepfakes and self-driving cars? One must have an opinion on the algorithms mimicking the human brain.
1. 6 Biggest Limitations of Artificial Intelligence Technology
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
2. Implementing The Perceptron Algorithm From Scratch In Python
3. A list of artificial intelligence tools you can use today — for businesses (2/3)
4. Five Successful AI and ML Use Cases In Manufacturing
How can manufacturers put artificial intelligence to work in the industry? In this article, you will find five possible applications of Machine learning and Deep learning to industrial processes optimization.
5. Deep Learning vs Machine Learning: A Simple Explanation
6. 5 Cool Python Project Ideas For Inspiration
In the past few years, the programming language that has got the highest fame across the globe is Python. The stardom Python has today in the IT industry is sky-high. And why not? Python has got everything that makes it the deserving candidate for the tag of- “Most Demanded Programming language on the Planet.” So, now it’s your time to do something innovative.
7. What I Learned Trying to Predict the Price of Cryptocurrencies
A few days ago, I presented a webinar about price predictions for cryptocurrencies. The webinar summarized some of the lessons we have learned building prediction models for crypto-assets in the IntoTheBlock platform. We have a lot of interesting IP and research coming out in this area but I wanted to summarize some key ideas that can result helpful if you are intrigued by the idea of predicting the price of crypto-assets.
8. The Best Slack Groups for Data Scientists to Join
The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups.
9. My Time at NUS, Singapore
Singapore is home to some of the best schools in the field of Computer Science, specifically Artificial Intelligence. The cutting edge research going on there is unparalleled. Colleges like Nanyang Technological University (NTU) and National University of Singapore (NUS) have a great reputation all over the world for their CS programs.
10. Top 5 Trends of Artificial Intelligence (AI) 2021
To estimate the trends of Artificial Intelligence (AI) 2021, we need to remember that 2018, 2019 and 2020 witnessed a multitude of platforms, applications, and tools which are based on artificial intelligence and machine learning.
11. Data Set and Data Augmentation for Face Detection and Recognition
When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first.
12. Difference between Artificial Intelligence, Machine learning, and deep learning
The development in the field of technology has enhanced over the years. With time, we get terms like Artificial Intelligence, machine learning, and deep learning in technology. We often confuse in these terms and define them similarly. But it is not a precise definition as these terms are different from each other. If you do not want to make this mistake again, then you must read out this article. Here we are going to discuss the difference in these three terms AI, ML, and Deep learning.
13. How to Use Model Playground for No-Code Model Building
We're launching Model Playground, a model-building product where you can train AI models without writing any code yourself. Still, with you in complete control.
14. Why You Should Use Deep Learning - A Thread
Rich Harang explains why you should use deep learning.
15. The Implications of Open-Source AI: Should You Release Your AI Source Code Publicly?
In this article, I will share my thoughts on why it's better and safer to bring the new AI tech into the hands of business rather than release it into the wild.
16. DJL: Deep Java Library and How To Get Started
Want to get your hands dirty with Machine Learning / Deep Learning, but have a Java background and not sure where to start? Then read on! This article is about using an existing Java skillset and ramp-up your journey to start building deep learning models.
17. 5 Papers on Face Recognition Every Data Scientist Should Read
Facial recognition, is one of the largest areas of research within computer vision. This article will introduce 5 face recognition papers for data scientists.
18. How to Interpret A Contour Plot
Contour Plot
19. Deploying Deep Learning Models with Model Server
Learn how to deploy deep learning models with Model Server.
20. How To Create A Simple Neural Network Using Python
I built a simple Neural Network using Python that outputs a target number given a specific input number.
21. What's The Best Image Labeling Tool for Object Detection?
An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools.
22. 10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020
23. What is an RNN (Recurrent Neural Network) in Deep Learning?
RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks.
24. Deep Learning Chatbots: Everything You Need to Know
When you’re creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods.
25. Flax: Google’s Open Source Approach To Flexibility In Machine Learning
Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax.
26. Embeddings at E-commerce
27. Image to Image Translation and Segmentation Tutorial
In this article and the following, we will take a close look at two computer vision subfields: Image Segmentation and Image Super-Resolution. Two very fascinating fields.
28. An Old Statistical Trick Might Help Better Explain the Apparent Correlation Between Bitcoin and Gold
The relationship between Bitcoin and Gold is one of the dynamics that seems to constantly capture the minds of financial analysts. Recently, there have been a series of new articles claiming an increasing “correlation” between Bitcoin and Gold and the phenomenon seems to be constantly debated in financial media outlets like CNBC or Bloomberg.
29. My Experiments And How To Start with Machine Learning
30. Can Graph Neural Networks Solve Real-World Problems?
In this article, we will learn about GNNs and its structure as well as its applications
31. Federated Learning: A Decentralized Form of Machine Learning
Major companies using AI and machine learning now use federated learning – a form of machine learning that trains algorithms on a distributed set of devices.
32. AI Facts Every Dev Should Know: Artificial intelligence is older than you, probably
The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future.
33. Neural Machine Translation: Using Open-NMT for Training a Translation Model
34. A Roundup Review of the Latest Deep Learning Books
35. This AI Creates Videos From a Couple of Images
Researchers created a simple collection of photos and transformed them into a 3-dimensional model.
36. Takeaways And Quotes From The World’s Largest Kaggle GrandMaster Panel
37. Summarizing Most Popular Text-to-Image Synthesis Methods With Python
Comparative Study of Different Adversarial Text to Image Methods
38. Facebook's Deepfake Challenge That Will defeat Deepfakes. Hopefully.
Nowadays, we are seeing a new wave and great advancements in different technologies. Things like Deep Learning, Computer Vision, and Artificial Intelligence are improving every single day. And Researchers and scientists are having amazing use-cases with these technologies which can change the direction of our world.
39. A Guide to Scaling Machine Learning Models in Production
40. Data Testing for Machine Learning Pipelines Using Deepchecks, DagsHub, and GitHub Actions
A complete setup of a ML project using version control (also for data with DVC), experiment tracking, data checks with deepchecks and GitHub Action
41. Gain State-Of-The-Art Results on Tabular Data with Deep Learning & Embedding Layers [A How To Guide]
Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.
42. Why I Dropped Out of College in 2020 to Design My Own ML and AI Degree
Most people would think I was crazy for starting 2020 as a college dropout (sorry mom!), but I wish I made this decision sooner.
43. Increase The Size of Your Datasets Through Data Augmentation
Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over.
44. Learning AI if You Suck at Math — P4 — Tensors Illustrated (with Cats!)
Welcome to part four of Learning AI if You Suck at Math. If you missed parts 1, 2, 3, 5, 6 and 7 be sure to check them out.
45. A list of artificial intelligence tools you can use today — for personal use (1/3)
Artificial Intelligence and the fourth industrial revolution has made some considerable progress over the last couple of years. Most of this current progress that is usable has been developed for industry and business purposes, as you’ll see in coming posts. Research institutes and dedicated, specialised companies are working toward the ultimate goal of AI (cracking artificial general intelligence), developing open platforms and the looking into the ethics that follow suit. There are also a good handful of companies working on AI products for consumers, which is what we’ll be kicking this series of posts off with.
46. Best Machine Learning Books You Should Read: 2020 Edition
These books cover the Introductory level to Expert level of knowledge and concepts in ML. These Books have some core factors about ML. Give them a try. Lets Start.
47. Artificial Intelligence, Machine Learning, and Human Beings
In a conversation with HackerNoon CEO, David Smooke, he identified artificial intelligence as an area of technology in which he anticipates vast growth. He pointed out, somewhat cheekily, that it seems like AI could be further along in figuring out how to alleviate some of our most basic electronic tasks—coordinating and scheduling meetings, for instance. This got me reflecting on the state of artificial intelligence. And mostly why my targeted ads suck so much...
48. Learning AI if You Suck at Math — P5 — Deep Learning and Convolutional Neural Nets in Plain…
Welcome to part five of Learning AI if You Suck at Math. If you missed parts 1, 2, 3, 4, 6, and 7 be sure to check them out!
49. An Introduction to “Liquid” Neural Networks
Liquid neural networks are capable of adapting their underlying behavior during the training phase.
50. When Machines Learn, Humans Lose Control
51. PixelLib: Image and Video Segmentation [Maybe just a Quick One]
PIxelLib: Image and video segmentation with just a few lines of code.
52. Human Intelligence or Artificial Intelligence? We Need Both.
Artificial intelligence (AI) has reached a tipping point, leveraging the massive pools of data gathered by every app, website, and device in our lives to make increasingly sophisticated decisions on our behalf. AI is at work in our inboxes sorting and blocking emails. It takes and processes our increasingly complex requests through voice assistants. It supplements customer support through chatbots, and heavily automates complex processes to reduce the workload for knowledge workers. Evidently, devices can adapt on the fly to human behavior.
53. Implementing different variants of Gradient Descent Optimization Algorithm in Python using Numpy
Learn how tensorflow or pytorch implement optimization algorithms by using numpy and create beautiful animations using matplotlib
54. How Predictive RBPs with DL can help get a vaccine for Corona faster?
The entire world is engulfed into a corona pandemic attack. At present, there are 191127 positive cases of noble COVID-19 infection all over the world with total fatalities of 7807 according to a report by the World Health Organization(WHO).
55. Black Mirror's 'Be Right Back' in Real Life: Clone Yourself as a Chatbot
Replika AI has created a platform where anyone, including people with zero knowledge of machine learning, can create and train a chatbot of their own.
56. Going From Not Being Able To Code To Deep Learning Hero
57. What's Next for Adaptive Neural Systems?
There is a trend in neural networks that has existed since the beginning of the deep learning revolution which is succinctly captured in one word: scale.
58. How I Designed My Own Machine Learning and Artificial Intelligence Degree
After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer.
59. Facial Recognition Comparison with Java and C ++ using HOG
HOG - Histogram of Oriented Gradients (histogram of oriented gradients) is an image descriptor format, capable of summarizing the main characteristics of an image, such as faces for example, allowing comparison with similar images.
60. No-Code Machine Learning inside Google Sheets
Introduction
61. Understanding The Importance Of Data For Machine Learning
Data is the most important and must-have food for machine learning. It can be any fact, text, symbols, images, videos, etc., but in unprocessed form. Let us see
62. A Complete Guide To The Machine Learning Tools On AWS
In this article, we will take a look at each one of the machine learning tools offered by AWS and understand the type of problems they try to solve for their customers.
63. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning!
*Nota: Contactar a Omar Espejel (omar@tsc.ai) para cualquier observación. Cualquier error es responsabilidad del autor.
64. The Dark Matter of AI: Common Sense Is Not So Common
There are still areas where AI lacks and causes problems and frustration to end-users, and these areas pose a great challenge for researchers right now.
65. Machine Learning for the ISIC Cancer Classification Challenge #2: Deep learning on AWS
(The full list of lesion types types to classify in the ISIC dataset. We’ll be focusing on Melanoma vs. non-Melanoma)
66. Creative AI ‘Shakes’ the Core of Humanity and Requires a Broader Discussion About Ethics
The boundary between machine and humans was clear. But now the machine has become creative! Can self expression still be at the core of our humanity?
67. Understanding A Recurrent Neural Network For Image Generation
The purpose of this post is to implement and understand Google Deepmind’s paper DRAW: A Recurrent Neural Network For Image Generation. The code is based on the work of Eric Jang, who in his original code was able to achieve the implementation in only 158 lines of Python code.
68. 8 Machine Learning Trends that Impact Business in 2021 and Beyond
Let’s discover the latest innovations in machine learning in 2021-2022 and go over various examples of how this technology can benefit you and your business.
69. How to Structure a PyTorch ML Project With Google Colab and TensorBoard
Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard
70. How to Perform MNIST Digit Recognition with a Multi-layer Neural Network
Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it looks like. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits.
71. Emotion AI, ML, and Deep Learning: A Brief Introduction
72. Where to Learn Machine and Deep Learning for Free
73. Beat The Heat with Machine Learning Cheat Sheet
If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?
74. Computer Vision Could Improve Health and Workplace Safety
Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers.
75. 20 Best Machine Learning Resources for Data Scientists
76. Use plaidML to do Machine Learning on macOS with an AMD GPU
Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.
77. Is GPU Really Necessary for Data Science Work?
A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more).
78. Why It’s Very Difficult to Create AI-Based Slow Motion
Over the last few years a number of open source machine learning
projects have emerged that are capable of raising the frame rate of
source video to 60 frames per second and beyond, producing a smoothed,
'hyper-real' look.
79. Retraining Machine Learning Model Approaches
Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation
80. Detecting Humans in Smart Homes with Computer Vision
Learn more about OpenCV, how you can use it to identify and track people in real-time, and what challenges you can meet.
81. Karate Club a Python library for graph representation learning
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
82. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models?
Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is
83. How Does "Hey Siri!" Work Without Your iPhone Listening To You At All Times?
Ever wondered if our phone can detect the “Hey Siri!” command anytime and interpret it, is it recording our daily life conversations too?
84. Image Annotation Types For Computer Vision And Its Use Cases
There are many types of image annotations for computer vision out there, and each one of these annotation techniques has different applications.
85. Why Python is Best Programming Language for Data Science & Machine Learning?
If you want to become a Data Scientist and are curious about which programming language should you learn then you have come to the right place.
86. MIDAS: A State-of-the-Art Model for Anomaly Detection in Graphs
In machine learning, hot topics such as autonomous vehicles, GANs, and face recognition often take up most of the media spotlight. However, another equally important issue that data scientists are working to solve is anomaly detection. From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.
87. How to Remove Gender Bias in Machine Learning Models: NLP and Word Embeddings
Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings.
88. The Awesome Duo: 6 Cases of How FinTech Benefits From AI
Photo by Alice Pasqual on Unsplash
89. How I Got to Top 24% on a Kaggle Text Classification Challenge Without Writing a Single Line of Code
In this post, we will see how to use the platform and get a submission that achieves a respectable 83% Accuracy on the test set.
90. Eight Awesome AI Youtube Videos Under 10 Minutes
Machine learning educational content is often in the form of academic papers or blog articles. These resources are incredibly valuable. However, they can sometimes be lengthy and time-consuming. If you just want to learn basic concepts and don’t require all the math and theory behind them, concise machine learning videos may be a better option.
91. Build a Custom-Trained Object Detection Model With 5 Lines of Code
These days, machine learning and computer vision are all the craze. We’ve all seen the news about self-driving cars and facial recognition and probably imagined how cool it’d be to build our own computer vision models. However, it’s not always easy to break into the field, especially without a strong math background. Libraries like PyTorch and TensorFlow can be tedious to learn if all you want to do is experiment with something small.
92. Interviews with My Machine Learning Heroes
Meta Article with links to all the interviews with my Machine Learning Heroes: Practitioners, Researchers and Kagglers.
93. How to Build a Training Pipeline on Multiple GPUs
In the current big data regime, it is hard to fit all the data into a single CPU.
94. NEON.LIFE: Meet Your Digital Avatar from Samsung
Anyone watched Blade Running 2049 must remember ‘Joi’, the pretty and sophisticated holographic projection of an artificial human. She speaks to you, helps you with house affairs, tells jokes to you, keeps you accompanied, and some more… just like a real human. She even has her own memory with you and developed character over time. Except, ‘she’ is not human. She is just a super complicated ‘modeling’ of a real human that can speak, act and react like one. Yet still, quite some people secretly wish that they could also have their own ‘Joi’. Well, she might not be as far away as you think. Enter NEON, Samsung’s new artificial human.
95. Best Resources To Learn Machine Learning And AI
“Anybody can code” , I know this sentence sounds cliche so let me give you another one “Anybody can learn AI”. Well, know it sounds overwhelming except if you are not a PhD or a mad scientist.
96. Introduction To Maths Behind Neural Networks
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
97. Top 10 Data Science Project Ideas for 2020
98. Training an Image Classifier From Scratch in 15 Minutes
99. B2B Sales Is Broken. New Tech Can Help
Closing b2b deals is difficult. People are not buying aggressive selling techniques. Existing sales softwares aren't helping. New tech can help.
100. Amazon ML Services: A Deep Dive Into AWS SageMaker
SageMaker is a fully managed service that enables developers to build, train, test and deploy machine learning models at scale.
101. Creating neural networks without human intervention
…And where is the blockchain in it?
102. Basic Use Cases of AI, ML, Deep Learning and Internet of Things
103. Neutron: A $4000 RTX 2080Ti (MSI) Deep Learning box (8700k/64GB/2080Ti)
104. 11 Awesome (and Worrisome) Applications of AI
For years AI was touted to be the next big technology. Expected to revolutionize the job industry and effectively kill millions of human jobs, it became the poster child for job cuts. Despite this, its adoption has been increasingly well-received. To the tech experts, this wasn’t really surprising given its vast range of use cases.
105. Getting Started With Pytorch In Google Collab With Free GPU
106. Why AI + Blockchain Make Sense?
In today’s world, it is impossible not to acknowledge the impact of technology on development and organizational growth. The use of technology is practically indispensable; it is present in every sector and industry, in small, medium, or large enterprises.
107. Get Machine Learning Training Data Using The Lionbridge Method [A How-To Guide]
In the field of machine learning, training data preparation is one of the most important and time-consuming tasks. In fact, many data scientists claim that a large portion of data science is pre-processing and some studies have shown that the quality of your training data is more important than the type of algorithm you use.
108. Living in the world of AI - The Human Transformation
Today, if you stop and ask anyone working in a technology company, “What is the one thing that would help them change the world or make them grow faster than anyone else in their field?” The answer would be Data. Yes, data is everything. Because data can essentially change, cure, fix, and support just about any problem. Data is the truth behind everything from finding a cure for cancer to studying the shifting weather patterns.
109. Approach Pre-Trained Deep Learning Models With Caution
Pre-trained models are easy to use, but are you glossing over details that could impact your model performance?
110. Demystifying Different Variants of Gradient Descent Optimization Algorithm
111. How I Built an AI to Detect License Plate Number Registration (ANPR)
This Car Mod Is A Privacy Nightmare! (AI Number Plate Reader with Python, Tensorflow, OpenCV, OpenALPR)
112. Top AI and ML YouTube Channels for Data Scientists to Subscribe to
Subscribe to these Machine Learning YouTube channels today for AI, ML, and computer science tutorial videos.
113. AI and Machine Learning for Manufacturing Industry: Use Cases
Artificial Intelligence(AI) has already proven to solve some of the complex problems across the wide array of industries like automobile, education, healthcare, e-commerce, agriculture etc. and yield greater productivity, smart solutions, improved security and care, business intelligence with the aid of predictive, prescriptive and descriptive analytics. So what can AI do for Manufacturing Industry?
114. How You Can Make a Naruto Hand Signs Classifier using Deep Learning
Introduction: (How I got the idea and the process of how the dataset was developed)
115. Driver Drowsiness Detection System: A Python Project with Source Code
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.
116. Introductory Guide To Real-time Object Detection with Python
117. Natural Language Processing: Explaining BERT to Business People
<TLDR> BERT is certainly a significant step forward in the context of NLP. Business activities such as topic detection and sentiment analysis will be much easier to create and execute, and the results much more accurate. But how did you get to BERT, and how exactly does the model work? Why is it so powerful? Last but not least, what benefits it can bring to the business, and our decision to integrate it into the sandsiv+ Customer Experience platform.</TLDR>
118. Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial
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119. The World's Most Powerful Deepfake Model was Just Released by Google
This AI can reconstruct, enhance and edit your images!
120. 10 Best Entry Level Machine Learning Tutorials
The field of machine learning is becoming easier and easier to enter thanks to readily available tools, a wide range of open source datasets, and a community open to sharing ideas and giving advice. Almost everything you need to get started is online; it's just a matter of finding it.
121. Deep Learning & Artificial Neural Networks: Solving The Black Box Mystery
I often hear people talking about neural networks as something as a black-box that you don’t understand what it does or what they mean. Actually many people can’t understand what they mean by that. If you understand how back-propagation works, then how is it a black-box?
122. 10 Best + Free Machine Learning Courses Collection
Here's a compilation of some of the best + free machine learning courses available online.
123. 6 important Python Libraries for Machine Learning and Data Science
In this guide, we’ll show the must know Python libraries for machine learning and data science.
124. 👨🔬️ Top 10 Data Scientist Skills to Develop to Get Yourself Hired
List of Top 10 Data Scientist skills that guaranteed employment. As well as a selection of helpful resources to master these skills
125. What Is Edge AI?
126. But Is It Art?—AI and the Algorithms vs. Artists Debate
There is a common belief among techies these days that with the arrival of AI and algorithms, professions such as those that of artists are becoming extinct. This is a misconception.
127. How To Apply Machine Learning And Deep Learning Methods to Audio Analysis
To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page.
128. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1)
Photo by Michael on Unsplash
129. Using Reinforcement Learning to Build a Self-Learning Grasping Robot
Tips and tricks to build an autonomous grasping Kuka robot
130. Top 25 Quotes from ML Heroes Interviews (+ an exciting announcement!)
Re-boot of “Interview with Machine Learning Heroes” and collection of best pieces of advice
131. Machine Learning 101: How And Where To Start For Absolute Beginners
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
132. Top 12 Javascript Libraries for Machine Learning
Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry.
133. College Admissions: How AI Can Help Fight Biases
This article is co-authored by Alex Stern & Eugene Sidorin.
134. Understanding Convolution Neural Networks
135. Building an End-to-End Speech Recognition Model in PyTorch with AssemblyAI
This post was written by Michael Nguyen, Machine Learning Research Engineer at AssemblyAI. AssemblyAI uses Comet to log, visualize, and understand their model development pipeline.
136. Procesando Datos Para Deep Learning: Datasets, visualizaciones y DataLoaders en PyTorch
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137. Unpredictability of Artificial Intelligence
The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed.
138. It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)
Recommendation algorithms have naturally penetrated to every part of information we get from the internet, from your basic search results on Google to your social media news feed on Instagram.
139. No Human Being Can Beat Google`s AlphaGo, and It’s a Good Thing
(Source: Netflix)
140. Another Self-Driving Car Accident: New AI Development Lesson To Learn From
(Source: https://blogs.nvidia.com)
141. 'El transformador ilustrado' una traducción al español
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142. Answering the 12 Most Common Questions About Python
Python is an open-source high-level programming language that is easy to learn and user-friendly. It is one of the first choices of many programmers be it a beginner or experienced. So, today we have prepared a list of most asked questions on Python programming language.
143. Image Segmentation: Tips and Tricks from 39 Kaggle Competitions
Imagine if you could get all the tips and tricks you need to hammer a Kaggle competition. I have gone over 39 Kaggle competitions including
144. Object Detection Using Single Shot MultiBox Detector (A Case Study Approach)
This blog post delivers the fundamental principles behind object detection and it's algorithms with rigorous intuition.
145. Combining CNNs, GANs, and Transformers to Outperform Image-GPT
Researchers combined the efficiency of GANs and convolutional approaches with the expressivity of transformers to outperform Open AI's Image-GPT
146. Introducing ML News
I know.
147. Machine Learning: What Does The Future Look Like?
148. Top 10 Computer Vision Papers of 2020
This is a video of the 10 most interesting research papers on computer vision in 2020.
149. #Switch2Swift for Deep Learning
If you are interested, what the recent fast.ai advanced and closed Deep Learning Class had to say about Google’s Swift for Tensorflow project, you might find this post interesting. Even if you attended the class, you should find here hopefully a good overview (with links into the class, presentations, and additional material), what Swift for Tensorflow is and why it might be relevant.
150. VOGUE by Google, MIT, and UW: The AI-Powered Online Fitting Room
Google used a modified StyleGAN2 architecture to create an online fitting room where you can automatically try-on any pants or shirts you want using only an ima
151. How Machine Generated Virtual Assistants can 10x Your Productivity in 2022
AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation machine learning, AR/VR and data science.
152. Machine-Learning Neural Spatiotemporal Signal Processing with PyTorch Geometric Temporal
PyTorch Geometric Temporal is a deep learning library for neural spatiotemporal signal processing.
153. "You may also like..." How To Use Convolutional Neural Networks
How to use a Convolutional Neural Network to suggest visually similar products, just like Amazon or Netflix use to keep you coming back for more.
154. Stateless vs Stateful LSTMs in Machine Learning
A brief description of Stateful and Stateless LSTM (one of the sequence modeling algorithms)
155. Deep Learning From Scratch Series: A Simple Neural Network [Part 1]
Photo from Pinterest Here -> this screenshot comes from a Martin Episode that you can watch here and get a good laugh 😂
156. How AI Is Transforming Your Smartphone
The tech industry and the world are relying on artificial intelligence to solve big problems such as cybersecurity, healthcare and sustainability.
157. How Amazon Uses Deep Learning to Improve Buying Experience
Up to 80 percent of customer interactions are managed by AI today.
158. Data Signals vs. Noise: Misleading Metrics and Misconceptions About Crypto-Asset Analytics
The steady growth in the crypto-asset space has increased the need and popularity of market intelligence/analytics products. However, like any other new asset class, the methodologies and techniques to extract meaningful intelligence about crypto-assets are going to take some time to mature. Fortunately, the crypto market was born in the golden age of data science and machine learning so it has a shot at building the most sophisticated generation of market intelligence products ever seen for an asset class. Paradoxically, it seems that we prefer to remain lazy and come up with half-baked analytics that have the mathematical rigor of a fifth grade class.
159. Why Use Pandas? An Introductory Guide for Beginners
Pandas is a powerful and popular library for working with data in Python. It provides tools for handling and manipulating large and complex datasets.
160. Join the $100,000 Kaggle Competition to Revolutionize Education
Parents and teachers have known for centuries that the best
education is delivered one-on-one by an experienced educator. But that is
expensive, labor intensive and cannot scale.
161. Artificial Intelligence: Time to Terminate the Terminator Tale?
With outdated dystopian movies like Terminator making headlines for possibly predicting the future and with companies like Google already releasing Artificial Intelligence (AI) tools and bots, is it time to rethink the Terminator narrative. As we move towards a world that is increasingly digitized, we must work to truly understand what it means to have and use AI.
162. Checking If Your Headline A Clickbait: A How-To Guide
For those who don’t know what that is… It is basically a magical tool that allows anyone to take existing AI models and train them for their own data, however, small the dataset maybe. Sounds good, right?
163. The GPUs for Deep Learning: NVIDIA vs AWS vs Azure and More
Take a deeper dive into what a GPU is, when you should use it or shouldn’t for Deep Learning tasks, and what is the best GPU on-premises and in the cloud in 202
164. A Beginners Guide to the Gradient Descent Algorithm
The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. It follows the steepest descent approach. That is it moves in the negative gradient direction to find the local or global minima, starting out from a random point. We use gradient descent to reach the lowest point of the cost function.
165. 70-Page Report on the COCO Dataset and Object Detection [Part 2]
This blog is part 1 of (and contains a link to) a 70+ page report was created to quickly find data resources and/or assets for a given dataset and a specific ta
166. Data Science with Substance: Best Massive Open Online Courses
Benjamin Obi Tayo, in his recent post "Data Science MOOCs are too Superficial," wrote the following:
167. Top Tips For Competing in a Kaggle Competition
Hi, my name is Prashant Kikani and in this blog post, I share some tricks and tips to compete in Kaggle competitions and some code snippets which help in achieving results in limited resources. Here is my Kaggle profile.
168. How Will Data Augmentation Affect Machine Learning?
This article about the future of data augmentation was written entirely by GPT-J. It is published here unedited. You can try GPT-J here for free.
169. The Dawn of the Transformer Neural Networks
Why are GPT-3 and all the other transformer models so exciting? Let's find out!
170. 5 Reasons Our Cities Are Not Full of Autonomously Flying Drones (Yet!)
We are slowly but surely moving towards a world where autonomous drones will play a major role. In this article, I will show you what stopes them today.
171. Neural Entity Linking and How JPMorgan Chase Plans to Use it
This article summarizes the problem statement, solution, and other key technical components of the paper: End-to-End Neural Entity Linking in JP Morgan Chase
172. Reinforcement Learning: 'Practice Makes a Machine Perfect'
Reinforcement learning is the fastest growing branches of machine learning. Embark your RL journey by getting a soft introduction to reinforcement learning now.
173. How We Automate 80-100% of Media Workflows with Cognitive Computing
Here's how you can use cognitive computing to automate media & entertainment workflows and stramline video production.
174. Merging Datasets from Different Timescales
One of the trickiest situations in machine learning is when you have to deal with datasets coming from different time scales.
175. Deploy Computer Vision Models with Triton Inference Server
There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics.
176. Predicting An Image Being Doodled, in Real Time - How We Built It.
(Mind you, this is not a tutorial, I will be posting one soon though!)
177. Introduction to My Computer Vision Project: ArtLine
ArtLine is based on Deep-Learning algorithms that will take your image input and transform it into a line art. I started this project as fun project but was excited to see how it turned out. The results from this model are so good that it is almost equal to the line art by an artist.
178. In Conversation with a Former NASA Machine Learning Engineer
An interview with Cohere's deep learning engineer Kuba Perlin and how he navigated his career into AI research.
179. From Issac Asimov to Decalogue
You shall have no other programmers but me.
180. Spoken Language Understanding (SLU) vs. Natural Language Understanding (NLU)
Differences between SLU (Spoken Language Understanding) and NLU (Natural Language Understanding). Top FOSS and paid engines and their approach to SLU.
181. Toon Filters And Video Transformation in EbSynth [Part 2]
182. Signal DeNoising using Auto Encoders
A project that added Additive White Gaussian Noise to a sinusoidal signal before training machine learning networks to denoise it effectively as a challenge.
183. 5 Things I Learned from Google’s New ML-Powered Recorder App
There are tons of audio recording apps in the app store, but you know things will be a bit different if Google developed a brand new one. Google recently released a new ‘Recorder’ app that is powered by its state-of-the-art Machine Learning algorithm that can transcribe what it hears with impressive precision in real-time. This is not the first time Google tried to bless its product with some AI ‘superpower’. Some of their prior attempts failed (I’m talking to you Google Clips!) and some had quite formidable success, for example, Google’s Pixel phone camera app.
184. Last Week in AI
Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it using this link. Please do so, our guys worked really hard on this.
185. Meet the Writer: Hacker Noon's Contributor Craig S. Smith, Host of the Eye on AI Podcast
Former New York Times foreign correspondent turns to deep tech.
186. The Ethics of Machine Learning: Understanding the Role of Developers and Designers
We know that the whole world is fascinated by the tools that are using Machine learning and deep learning algorithms and they are fun to use.
187. WTF is Deep Learning? Explanation for Non Tech People [Only Words and Pictures]
Hello, there! In the next few minutes, we'll talk about a subject called Deep Learning. Have you heard about it?
188. Deep Learning is Already Dead: Towards Artificial Life with Olaf Witkowski
Olaf Witkowski is the Chief Scientist at Cross Labs, which aims to bridge the divide between intelligence science and AI technology. A researcher of artificial life, Witkowski started in artificial intelligence by exploring the replication of human speech through machines. He founded Commentag in 2007, and in 2009 moved to Japan to continue research, where he first became interested in artificial life.
189. Conversational AI is Changing the Way We Interact With Data
Large language models like ChatGPT are making it easier to manage data. Akkio has come up with an LLM-based tool to manage tabular data using conversational AI.
190. Language Modeling - A Look at the Most Common Pre-Training Tasks
This article is about putting all the popular pre-training tasks used in various language modelling tasks at a glance.
191. On Guard Against COVID-19: AI Projects That Deserve a Shout-Out
I've seen many blogs and articles saying the artificial intelligence (AI) will save the humankind from the 2019-nCoV (a.k.a. COVID-19) pandemic. I'm sorry for breaking it, but AI will not save us from the Coronavirus. Physical distancing and handwashing will. However, what it can help with is flattening the curve. We badly need to slow down the rate of the virus spread in each and every community to give local hospitals time to deal with both the infected patients and the capacity to handle the ever-growing loads of patients. And that's where AI can come in handy. On a global scale, effective AI and machine learning solutions for the Coronavirus timely detection and control give time to hundreds of R&D teams all over the globe working to create a vaccine against the virus.
192. The Crucial Role of Machine Learning in Cybersecurity
In 2019, more than 627 million online records were comprised due to hacking and other types of cyber attacks. This is a pretty staggering number to anyone who has made an online transaction, but the amount of attacks that were stopped is much higher, so it’s worth some optimism. As COVID-19 has pushed many companies into the remote work world, online transactions and records are growing exponentially, and most experts believe that remote work will continue to be very popular even after stay-at-home orders get lifted and life goes back to some form of normal.
193. Small Object Detection in Computer Vision: The Patch-Based Approach
How to carry out small object detection with Computer Vision - An example of finding lost people in a forest.
194. An Intro to Edge Computer Vision: Technologies, Applications, Use Cases and Key Models
introduction to computer vision technologies, applications, use cases and key models.
195. On Investing In People Over Ideas or Apps with AI YouTuber Louis Bouchard
An interview with Louis, an AI YouTuber known as What’s AI, and a research scientist at designstripe.
196. 2020 World University Ranking: AI Safety
This Top 10 ranking is produced by Dr. Roman V. Yampolskiy (University of Louisville) and is based solely on his biased opinion. (To reduce bias University of Louisville is Not Ranked) To a certain degree the ranking is also based on perceived reputation, Google scholar listings under AI Safety, quality and quantity of papers, Google search rankings, impact of publications and number of scholars working in the area full time. Many other universities do work on AI Safety but are not ranked this year. By definition the list excludes all industry labs.
197. Optimize Model Training with a Data Streaming Client
Were you ever annoyed when you had to pull a massive dataset (versioned using DVC) before training your model?
198. Jeremy Howard’s fast.ai vs Andrew Ng’s deeplearning.ai - Are They That Different From Each Other?
How Not to ‘Overfit’ Your AI Learning by Taking Both fast.ai and deeplearning.ai courses
199. 5 ML Security Challenges Demanding our Unwavering Attention
As per Gartner, almost 80 percent of every emerging technology will have Artificial Intelligence as the backbone by the end of 2021. Building secure software is a no mean feat. Amid the lingering cybersecurity threats and the potential challenges posed by the endpoint inadequacies, the focus is continuously shifting towards machine learning and the relevant AI implementations for strengthening the existing app and software security standards.
200. How To Use Keras To Train Neural Networks
Keras is a deep learning framework for Python for building neural networks and training them on datasets. It can leverage GPUs and CPUs for training algorithms.
201. Why Learning PyTorch Can Make you a Better Engineer
Pytorch is a powerful open-source deep-learning framework that is quickly gaining popularity among researchers and developers
202. Taking Deepfakes Seriously
Artificial intelligence is quickly becoming a reality, but how does it affect our society. Is it something we should fear or embrace? Read to learn more.
203. TLDR Newsletter Week of August 12th Highlights
Sponsor: Scraper API's 5 Tips for Web Scraping
204. New Major Release for Nebullvm Speeds Up AI Inference by 2-30x
Nebullvm 0.3.0 features more deep learning compilers and now supports additional optimization techniques, including quantization and half precision.
205. The Best AI Articles of October 2022
Support vector machines, decision trees, and AI-generated content are some of the topics in the best AI articles of October.
206. What is Natural Language Processing? A Brief Overview
Natural language processing (NLP) is a subfield of artificial intelligence. It is the ability to analyze and process a natural language.
207. A Full Time ML Role, 1 Million Blog Views, 10k Podcast Downloads: A Community Taught ML Engineer
As the title mentions, this is a quick recap of a community taught ML Engineer's journey.
208. 5 Essential Product Classification Papers for Data Scientists
Product categorization/product classification is the organization of products into their respective departments or categories. As well, a large part of the process is the design of the product taxonomy as a whole.
209. The Art of Saying NO
“I used to brag about talks I gave; now I brag about talks I turned down.”
210. How to Build Feedforward Neural Networks: A Step-by-Step Guide
Create a deep learning framework from scratch!
211. It’s Time to Reap the Rewards for Reading in Web3
It's time for deep reading in Web3
212. Ways To Overcome Linguistic Barriers with Language Technologies
COVID-19 has impacted every other industry and has made people adopt newer norms. The traditional translation industry is no different. Several disruptions have been introduced to keep things moving, thanks to Big data and machine translation technologies that have enabled the world to do business as usual.
213. Why Deep Learning Is Still Too Difficult
While deep learning has great potential, building practical applications powered by deep learning remains to be too expensive and too difficult for many organizations. In this article, we will describe some of the challenges to broader adoption of deep learning. We will also explain how those challenges differ from those of traditional machine learning systems, and the path forward to making deep learning more widely accessible.
214. The Challenges of Running Computer Vision on the Edge
Artificial intelligence (AI) is the field of making computers able to act intelligently, to make decisions in real environments that will have favorable outcomes.
215. Introducing ML News
I know.
216. JPEG Sucks but Deep Learning Can Help It Improve
In this post, I’ll show how you can reduce image sizes by an additional 20–50% with a single line of code.
217. Will Deepfakes Be Part of Our Lives?
I was receiving a particular forwarded meme of a famous Hollywood actor over WhatsApp from so many of my contacts since last a few days. This one might have gone viral. It superimposes the actor’s face on the body of Superhero Hulk and makes him do some nasty stuffs. Oh! Quite ridiculous but people are liking it. The video was made with extreme perfection and the finishing touch was superb. I came to know later on that it was made by an internet user only.
218. Selfie Biometrics: Exploring Face Recognition & Liveness Technologies for Mobile Apps
Selfie biometrics will very soon become our verification standard.
219. What are Adversarial AI Attacks and How Do We Combat Them?
Deep learning models are capable of performing on par with, if not exceeding, human levels, at a variety of different tasks and objectives.
220. Creating a Dataset Sucks. Here's What I've Learned to Make it a Little Bit Easier
Multiple models trained on your data perform surprisingly poorly, despite having decent metrics on the validation set. The code seems fine, so you decide to take a closer look at your training data. You check a random sample - the label is wrong. So is the next. Your stomach sinks and you start looking through your data in batches*. Thirty minutes later, you realize that x% of your data is incorrect.
221. How Deep Learning Can Help Quantify, Monitor, and Remove Marine Plastic: The DeepPlastic Way
Towards a generalized object detector capable of identifying and quantifying sub-surface plastic around the world
222. Tell If Your SMS is Spam
Introduction
223. AI and Automation, What's Next? A Take Over Or a Symbiosis?
Rethinking the future we want not the one that will befall us. We are in charge of our destiny.
224. Breaking into Deep Learning: Transforming the World Without Expert Input
Deep learning is a subdivision of machine learning in which Artificial Neural Networks (ANNs) learn from a huge influx of data to produce high-quality output.
225. Fine-Tuning Machine Learning Models with DVC Experiments for Transfer Learning
You can work with pretrained models and fine-tune them with DVC experiments.
226. Training Neural Networks with Gorgonia
Deep learning and neural networks are very interesting subjects and Go language supports this technology using the framework Gorgonia.
227. Machine Learning 101: How And Where To Start For Absolute Beginners
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
228. Alluxio Accelerates Deep Learning in Hybrid Cloud using Intel’s Analytics Zoo powered by oneAPI
229. 7 Must-Read Generative Models Papers from ICLR 2020
The International Conference on Learning Representations (ICLR) took place last week, and I had a pleasure to participate in it. ICLR is an event dedicated to research on all aspects of representation learning, commonly known as deep learning.
230. What Real Deep Learning Applied to Social Media Tells Us About the Crypto Market
Social and news media plays a relevant role in the dissemination of information related to crypto-assets. In a nascent financial market without established disclosure mechanisms, a lot of the relevant events about crypto-assets are distributed first in news and social media channel and, not surprisingly, the market remains incredibly susceptible to those channels.
231. Learn About Google DeepMind –
DeepMind may allude to two things: the innovation behind Google’s man-made reasoning (AI) venture, and the organization that is liable for it. The organization DeepMind is an auxiliary of Alphabet, the parent organization of Google.
232. Efficient Model Training in the Cloud with Kubernetes, TensorFlow, and Alluxio Open Source
233. 70-Page Report on the COCO Dataset and Object Detection [Part 3]
234. What Does the Future of Machine Learning Look Like?
In this article, we will discuss the future of machine learning and its value throughout industries, from automotive to healthcare and pharma industries.
235. Anomaly Detection from Fetal ECG — A Case Study of IOT Anomaly Detection using GAN
In this blog, we discuss about the role of Variation Auto Encoder in detecting anomalies from fetal ECG signals.
236. Blockchain and AI | A Disruptive Alliance
AI and Blockchain are among some of the most influential drivers of innovation today — a natural convergence is occurring.
237. Why Deep Learning is not Enough for Video Content Analysis
Deep Learning gets a ton of traction from technology enthusiasts. But can it match the effectiveness standards that the public hold it to?
238. Use Cascade Models to Get Better Speed and Accuracy in Computer Vision Tasks
Great way to improve your Computer Vision models metrics
239. Will Generative Models Be The Next Machine Learning Boom?
Machine Learning is a rapidly growing and very complex field of study. Generative Models might prove to be a new breakthrough that will make a new boom.
240. The Inevitable Symbiosis of Cybersecurity and AI
While improvements in AI and Deep Learning move forward at an ever increasingly rapid rate, people have started to ask questions. Questions about jobs being made obsolete, questions about the inherent biases programmed into the neural networks, questions about whether or not AI will eventually consider humans as dead-weight and unnecessary to achieve the goals they've been tasked programmed with.
241. 70-Page Report on the COCO Dataset and Object Detection [Part 1]
Quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection
242. Last Week in AI
Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it using this link. Please do so, our guys worked really hard on this.
243. 15 Must-read Machine Learning Articles for Data Scientists
As always, the fields of deep learning and natural language processing are as busy as ever. Despite many industries being hindered by the quarantine restrictions in many countries, the machine learning industry continues to move forward.
244. A Gentle Introduction to Data Augmentation
Data augmentation is a set of techniques used to increase the amount of data in a machine learning model by adding slightly modified copies of existing data.
245. Artificial Intelligence Business Opportunities: 10 Steps to Implement
Much like the rest of the world, Artificial Intelligence (A.I) has a 1% problem. Creating a smart algorithm is not yet a given for many entrepreneurs - why not?
246. 10 Computer Vision Startups on Product Hunt with the Most Upvotes
From self-driving cars and facial recognition to AI surveillance and GANs, computer vision tech has been the poster child of the AI industry in recent years. With such a collaborative global data science community, the advancements have come both from research teams, big tech, and computer vision startups alike.
247. This Open-Source Library Accelerates AI Inference by 5-20x in a Few Lines of Code
Nebullvm is an open-source library that can accelerate AI inference by 5-20x in a few lines of code, improving machine learning speeds without being complicated
248. Top 25 Publications About AI You Have To Read
Hey there Noonies! Hope the afternoon is going great with lots of code and coffee. Even I was just sitting by the window enjoying rain when suddenly the sky turned dark and I wanted to switch on the light to read my book better, but the switch is on the other side of the room! So, I just said, Hey Alexa, switch on the lights, and voila! After a while I switched on my TV and there it was, Gracie, helping out the covid patients and our front line superheroes amidst the pandemic. From a light switch to a pandemic, seems like artificial intelligence is slowly winning the world. Well, if you wanna join the race, here you go with the top stories on Artificial Intelligence on Hacker Noon.
249. GANs Training: Generate Images Following a Sketch
Control GANs outputs based on the simplest type of knowledge you could provide it: hand-drawn sketches.
250. Deepfakes: A Present Danger to Cybersecurity
Deepfakes are currently a concern, but over the next few of years, they're going to get worse.
251. "Give Your Team the Space to Develop Ideas," Chief AI Scientist Arash Azhand
Some time ago I had a chance to interview a great artificial intelligence researcher and Chief AI Scientist in Lindera, Arash Azhand.
252. AI Hotspots Across the US: Where AI Pros Are Thriving
Whether you used GPS to get to work or added a recommended add-on item to your online shopping cart, AI has likely touched your life in one way or another this very day. But does the increasing presence of AI in our day-to-day actually benefit us in more than just adding convenience to our lives? For tech pros, the answer is likely yes.
253. Introducing aasaan.ai: No-Code Yelp Sentiment Classification
Introduction
254. Comparative Study Of Best Time-Series Models For Pandemic Response
With the effect of the pandemic increasing every day and casting a vehemently toxic influence in almost all parts of the world, it becomes important how can we contain the spread of the disease. In an effort to combat the disease every country has increased not only their testing facility but also the amount of medical help and emergency and quarantine centers. Here in this blog, we try to model Single-Step Time Series Prediction, using Deep Learning Models, on the basis of Medical Information available for different states of India.
255. Data Science As A Career: 12 Steps From Beginner to Pro
12 steps for those looking to build a career in Data Science from scratch. Below there is a guide to action and a scattering of links to useful resources.
256. Top 25 Publications About AI You Have To Read
Hey there Noonies! Hope the afternoon is going great with lots of code and coffee. Even I was just sitting by the window enjoying rain when suddenly the sky turned dark and I wanted to switch on the light to read my book better, but the switch is on the other side of the room! So, I just said, Hey Alexa, switch on the lights, and voila! After a while I switched on my TV and there it was, Gracie, helping out the covid patients and our front line superheroes amidst the pandemic. From a light switch to a pandemic, seems like artificial intelligence is slowly winning the world. Well, if you wanna join the race, here you go with the top stories on Artificial Intelligence on Hacker Noon.
257. What Does a Deep Learning Architect at NVIDIA Do? (Video Interview)
An interview with Adam Grzywaczewski, senior data scientist at NVIDIA
258. The Issue Of Machine Ethics and Robot Rights
Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence/robotics communities. We will argue that the attempts to allow machines to make ethical decisions or to have rights are misguided. Instead we propose a new science of safety engineering for intelligent artificial agents. In particular we issue a challenge to the scientific community to develop intelligent systems capable of proving that they are in fact safe even under recursive self-improvement.
259. Artificial Intelligence, Machine Learning and Deep Learning Basics
In recent years, artificial intelligence (AI) has been the subject of intense exaggeration by the media. The Machine Learning and Deep Learning in Spanish Machine Learning (AA) and Learning Deep (AP), with the IA, have been mentioned in countless articles and media regularly outside the realm of purely technological publications. We are promised a future of smart chat bots, autonomous cars and digital assistants, a future sometimes painted in a gloomy tint and other times in a Utopian way, where jobs will be scarce and most economic activity will be managed by robots and machines. embedded with AI.
For the future or current Machine Learning practitioner, it is of vital importance to be able to recognize the signal in the noise, so that we are able to recognize and spread about the developments that are really changing our world and not the exaggerations commonly seen in the media. Communication. If, like me, you are a practitioner of Machine Learning, Deep Learning or another field of AI, we will probably be the people in charge of developing those intelligent machines and agents, and therefore, we will have an active role to play in this and future society.
260. How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML
How to start machine learning & Ways to keep up with the latest developments in Machine Learning.
261. Leveraging General Adversarial Networks for Material Sciences
Material scientists often face the challenge of figuring out how to effectively search the vast chemical design space to locate the materials with their desired properties. To address this challenge, many scientists have turned to artificial intelligence in the race to discover new and advanced materials.
262. Our Quest To Solve Inefficient Web Traffic Monetization
Have you heard or perhaps even tried new ways to purchase stuff you see on TV? You know, the ones that invite you to buy things you see while your favorite show is being aired? They offer you to shop through various user interaction mechanics that range from scanning a QR code shown at the corner of the TV screen to pressing a set of navigation buttons on the remote control to receive a text message with a link to the product. What a maze, I have to say.
263. Synthesizing Images of Marine Plastic Using Deep Convolutional Generative Adversarial Networks
A generative approach towards synthesizing images of marine plastic using DCGANs
264. The Ten Must Read NLP/NLU Papers from the ICLR 2020 Conference
The International Conference on Learning Representations (ICLR) took place last week, and I had a pleasure to participate in it. ICLR is an event dedicated to research on all aspects of representation learning, commonly known as deep learning. This year the event was a bit different as it went virtual. However, the online format didn’t change the great atmosphere of the event. It was engaging and interactive and attracted 5600 attendees (twice as many as last year). If you’re interested in what organizers think about the unusual online arrangement of the conference, you can read about it here.
265. Optical Character Recognition Technology for Business Owners
How to use Machine learning, Deep learning and Computer Vision for building Optical Character Recognition (OCR) solution for text recognition.
266. The Ultimate Toolbox Of ML Startups
Setting up a good tool stack for your Machine Learning team is important to work efficiently and be able to focus on delivering results. If you work at a startup you know that setting up an environment that can grow with your team, needs of the users and rapidly evolving ML landscape is especially important.
267. Activate: Beast Mode
There’s an astronomical difference between simply writing code and being a great developer.
268. Artificial Intelligence Technology Trends That Matter for Business in 2022
Discover the top AI trends that are increasing in 2022 and will determine how companies can leverage the AI technology in the future.
269. How to Get Started With Embeddings
Getting started with embeddings using open-source tools.
270. Interview with Andreas Mueller | CTDS.Show #72
CTDS.Show is a podcast where Sanyam Bhutani interviews his ML Heroes about their journey.
271. Data Science As A Career: 12 Steps From Beginner to Pro
12 steps for those looking to build a career in Data Science from scratch. Below there is a guide to action and a scattering of links to useful resources.
272. How Neural Networks Hallucinate Missing Pixels for Image Inpainting
When a human sees an object, certain neurons in our brain’s visual cortex light up with activity, but when we take hallucinogenic drugs, these drugs overwhelm our serotonin receptors and lead to the distorted visual perception of colours and shapes. Similarly, deep neural networks that are modelled on structures in our brain, stores data in huge tables of numeric coefficients, which defy direct human comprehension. But when these neural network’s activation is overstimulated (virtual drugs), we get phenomenons like neural dreams and neural hallucinations. Dreams are the mental conjectures that are produced by our brain when the perceptual apparatus shuts down, whereas hallucinations are produced when this perceptual apparatus becomes hyperactive. In this blog, we will discuss how this phenomenon of hallucination in neural networks can be utilized to perform the task of image inpainting.
273. How To Creat an Audible Object Detector [DIY Tutorial]
For people with vision problems.
274. We Released Modern Google-level Speech-to-Text Models
Our models are on par with premium Google models and also really simple to use.
275. The Art of Transformers: How AI Intuitively Summarizes Business Papers Using NLP
“I don’t want a full paper, just give me a concise summary of it”. Who hasn't found themselves in this situation, at least once? Sound familiar?
276. Jina, a Deep Learning-Powered Search Framework, Can Help You Build Your Neural Search
Use Jina to search text or images with the power of deep learning.
277. Top 5 Jaw-Dropping Applications of Deep Learning in Healthcare Sectors
In the real-world clinical environment, deep learning is steadily finding its way into innovative technologies and tools.
278. NVIDIA ADA: Train Your GAN With 1/10th of the Data
With this new training method developed by NVIDIA, you can train a powerful generative model with one-tenth of the images! Making possible many applications tha
279. The Hitchhikers's Guide to PyTorch for Data Scientists
PyTorch has sort of became one of the de facto standard for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.
280. Text Classification Models: All Tips And Tricks From 5 Kaggle Competitions
In this article (originally posted by Shahul ES on the Neptune blog), I will discuss some great tips and tricks to improve the performance of your text classification model. These tricks are obtained from solutions of some of Kaggle’s top NLP competitions.
281. Using Explainable AI in Decision-Making Applications
Here we explore the essence of explainability in AI and analyzing how applies to decision support systems in healthcare, finance, and other different industries
282. 5 Steps To Build Your Dynamic Pricing Engine
With the emergence of online platforms, B2B businesses have had to reconsider their pricing strategies. But, these same technologies help the organizations create dynamic B2B pricing models that bring substantial profits if implemented correctly. For example, an integrated sales and B2B pricing software can help sales reps negotiate with customers and reduce the processing period.
283. Spotting Monster Shrines and Finding Lost Treasures: 5 Genuinely Cool AI Projects
Finally, we’ve invented the sci-fi technology of the future! And what do we do? Make tech support chatbots and check insurance claims…
284. Classify Handwritten Digits using Deep learning with Tensorflow
285. A Simple Introduction to Edge AI
Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge.
286. Artificial Intelligence: Drawing Inspiration from Human Capabilities
This video is both an introduction to the recent paper Thinking Fast and Slow in AI by Francesca Rossi and her team at IBM, and to Luis Lamb's most recent paper
287. Eight Leaders Explain Their Model Training Libraries For The PyTorch Ecosystem
I started using Pytorch to train my models back in early 2018 with 0.3.1 release. I got hooked by the Pythonic feel, ease of use and flexibility.
288. The Overview of Prospective DL Algorithms: Ready-to-Use Solutions and Predictions for Future
A field that is bringing alot of commotion and noise is Artificial Intelligence. But something that really fascinates me is a subset of that field known as Artificial General Intelligence (AGI) or the holy grail of Artificial Intelligence.
289. Batch Processing Role in Deep Learning
With two common buzzwords in AI being Graphics Processing Unit (GPU) and Batch Processing, there is widespread need to run AI efficiently in production.
290. Exploring T5 Model : Text to Text Transfer Transformer Model
Recent years have seen a plethora of pre-trained models such as ULMFiT, BERT, GPT, etc being open-sourced to the NLP community. Given the size of such humungous models, it's nearly impossible to train such networks from scratch considering the amount of data and computation that is required. This is where a new learning paradigm "Transfer Learning" kicks in. Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
291. Debunking 4 Common Myths About Machine Learning
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve them.
292. Facial Recognition On Drone
A facial recognition demonstration using Keras, Tensorflow, python and the drone Tello, from DJI.
293. Why and How do We Split the Dataset
Dataset is one important part of the machine learning project. Without data, machine learning is just the machine, and learning is stripped from the title. Whic
294. Improve Your AI Training Data Using Self-Agreement Protocols
Finding, creating, and annotating training data is one of the most intricate and painstaking tasks in machine learning (ML) model development. Many crowdsourced data annotation solutions often employ inter-annotator agreement checks to make sure their labeling team understands the labeling tasks well and is performing up to the client’s standards. However, some studies have shown that self-agreement checks are as important or even more important than inter-annotator agreement when evaluating your annotation team for quality.
295. AI Safety And Security In The Context of US Government
On February 11th 2019 President of the USA signed an executive order on Maintaining American Leadership in Artificial Intelligence[1]. In it, the President particularly emphasized that the “… relevant personnel shall identify any barriers to, or requirements associated with, increased access to and use of such data and models, including … safety and security concerns …”. Additionally, in March, the White House announced AI.gov, an initiative for presenting efforts from multiple federal agencies all geared towards creating “AI for the American People”[2].
296. 6 Deep Learning Techniques for Stronger Cybersecurity
To help with the same, some experts have advised about the usage of deep learnings for Cybersecurity. Deep Learning is a crucial part of Machine Learning
297. Introduction To The Convolution
In this article, we are going to learn about the grayscale image, colour image and the process of convolution.
298. Why You Should Run Multiple Applications on the Same GPU (and Why it's so Difficult)
While GPUs are being used more and more, many users encounter the problem of not utilizing them properly.
299. The AI Monthly Top 3 Papers of October 2021
The 3 most interesting research papers of October 2021!
300. A Self-supervised Attention Mechanism To Help With Dense Optical Flow Estimation
Multi-object Tracking using self-supervised deep learning
301. LSTM Based Word Detectors
This article aims to provide the basics of LSTMs (Long Short Term Memory) and implements a word detector using the architecture.
302. How To Identify Trees with Deep Learning
Idea / inspiration
303. How Artificial Intelligence Is Reshaping the IT Industry
Artificial Intelligence has powerfully penetrated the way we live. It doesn’t only change the way we work but also reshaped how we used to live. Speaking of AI, it is one of the most interesting technologies that we’ve ever encountered.
304. Towards an ImageNet Moment for Speech-to-Text: A Deep Dive
Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google, Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable “in-the-wild” solutions.
305. Building Machine Learning Models With TensorFlow
In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models.
306. Deep Learning at Alibaba Cloud with Alluxio: How To Run PyTorch on HDFS
This tutorial shows how Alibaba Cloud Container team runs PyTorch on HDFS using Alluxio under Kubernetes environment. The original Chinese article was published on Alibaba Cloud's engineering blog, then translated and published on Alluxio's Engineering Blog
307. Solution To Value Agregation
A Solution to the Multi-Agent Value Alignment Problem
308. How the Retail Industry is Implementing Machine Learning and Deep Learning
Stores are changing. We see it happening before our eyes, even if we don’t always realize it. Little by little, they are becoming just one extra step in an increasingly complex customer journey. Thanks to digitalisation and retail automation, the store is no longer an end in itself, but a mean of serving the needs of the brand at large. The quality of the experience, a feeling of belonging and recognition, the comfort of the purchase… all these parameters now matter as much as sales per square meter, and must therefore submit themselves to the optimizations prescribed by Data Science and its “intelligent algorithms” (aka artificial Intelligence in the form of machine learning and deep learning).
309. Document Classification Process: 7 Pragmatic Approaches For Small Datasets
Document or text classification is one of the predominant tasks in Natural language processing. It has many applications including news type classification, spam filtering, toxic comment identification, etc.
310. 2 Years In The Life Of AI, ML, DL And Java - Part II
A follow-up post on the back of the post two-years ago with the title "Two Years In The Life Of AI, ML, DL And Java"
311. 6 GAN Architectures Every Data Scientist Should Know
Source: neptune.ai
312. CTDS: 1 Year anniversary | Kaggle Contest | CTDS.News Launch
In the Birthday AMA Episode, Sanyam Bhutani had shared a small series of "exciting updates" coming to CTDS.Show:
313. 3D Models at City Scale!
Last year we saw NeRF, NeRV, and other networks able to create 3D models and small scenes from images using artificial intelligence. Now, we are taking a small step and generating a bit more complex models: whole cities. Yes, you’ve heard that right, this week’s paper is about generating city-scale 3D scenes with high-quality details at any scale. It works from satellite view to ground-level with a single model. How amazing is that?! We went from one object that looked okay to a whole city in a year! What’s next!? I can’t even imagine.
314. Sentiment Analysis using Deep Learning techniques with India Elections 2019 — A Case study
Predict BJP Congress Sentiment using Deep Learning
315. The 2021 AI Rewind: HackerNoon Edition
A curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation
316. AI Rewind: A Year of Amazing Machine Learning Papers
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.
317. Is Subjective Beauty Something We Can Model with AI?
This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features.
318. Top Deep Learning Papers from This Year's ICLR Conference
Last week I had a pleasure to participate in the International Conference on Learning Representations (ICLR), an event dedicated to the research on all aspects of deep learning. Initially, the conference was supposed to take place in Addis Ababa, Ethiopia, however, due to the novel coronavirus pandemic, it went virtual. I’m sure it was a challenge for organisers to move the event online, but I think the effect was more than satisfactory, as you can read here!
319. How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML
How to start machine learning & Ways to keep up with the latest developments in Machine Learning.
320. What Did AI Bring to Computer Vision?
In this video, I will openly share everything about deep nets for computer vision applications, their successes, and the limitations we have yet to address.
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