Intuition of AI methods
The intuition behind neural networks
Biological vs artificial intelligence
Once you’ve seen the first 2 videos, I strongly advise to go play around on the Tensorflow Playground website. Modify the number of hidden layers, the number of neurons per layer, the activation functions, as well as the input features to see what is needed for what type of classification!
Understanding Reinforcement Learning
Time Consistency for automatic object detection
Explainable Pipeline Reinforcement Learning
Understanding differences in games' complexities
Neural Style Transfer
My Machine Learning Blog Posts
Other Interesting sources of information
I hereby refer to all nice source of information I usually use in order to get knowledge about machine learning, or any interesting topic. If you are interested in explanations about anything, made by performant video maker, please give a look at my Youtube following page.
Other nice sources of knowledge:
- Medium (any thought shared by experts on trending subjects)
- Towards Data Science (focused on Data Science)
- Wikiwand (makes Wikipedia looks beautiful)
- Lil’Log (Best Machine Learning blog)
- Brian Douglas’ site (Engineering and optimal control website with videos)
Summaries of papers
For scientific paper summaries, my goal is to share complete summary with all important information that should be highlighted, with some comments. Nowadays, a “popularized” (explained for a less scientific audience) version of the scientific paper is usually provided (through blog posts for example). When it’s the case, I provide the link. The difference between my summaries and these block-posts is that I usually try to provide every relevant scientific information (i.e. math equations and links) for scientifically understand the paper.
This work was first personal, to better dive into the paper that I have found astonishing so far, but I have finally decided to share this work that could be useful also to others. I hope this could help some people that want to quickly read them.
All the credits have to be given to the amazing work made by the actual researchers and authors.