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 newvick.com
Understanding Backpropagation Newvick's blog
| | programmathically.com
Understanding The Exploding and Vanishing Gradients Problem - Programmathically
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| | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights []
| | comsci.blog
Understanding and Building Neural Networks: A Step-by-Step Guide from Scratch | ML and robotics notes
3.5 parsecs away

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| | In this tutorial, we will learn two different methods to implement neural networks from scratch using Python: Extremely simple method: Finite difference Still a very simple method: Backpropagation
| | windowsontheory.org
Yet another backpropagation tutorial - Windows On Theory
2.0 parsecs away

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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...
| | vxlabs.com
Variational Autoencoder in PyTorch, commented and annotated. - vxlabs
17.1 parsecs away

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| I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time.