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 theorydish.blog
Backpropagation Chain Rule - Theory Dish
| | newvick.com
Understanding Backpropagation Newvick's blog
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| | I've been working my way through Andrej Karpathy's 'spelled-out intro to backpropagation', and this post is my recap of how backpropagation works.
| | windowsontheory.org
Yet another backpropagation tutorial - Windows On Theory
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| | (Updated and expanded 12/17/2021) I am teaching deep learning this week in Harvard's CS 182 (Artificial Intelligence) course. As I'm preparing the back-propagation lecture, Preetum Nakkiran told me about Andrej Karpathy's awesome micrograd package which implements automatic differentiation for scalar variables in very few lines of code. I couldn't resist using this to show how...
| | jingnanshi.com
Automatic Differentiation: Forward and Reverse
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| | Tutorial on automatic differentiation
| | jaketae.github.io
Convolutional Neural Network with Keras - Jake Tae
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| Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is...