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newvick.com | ||
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windowsontheory.org
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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... | |
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marcospereira.me
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| | | | | In this post we summarize the math behind deep learning and implement a simple network that achieves 85% accuracy classifying digits from the MNIST dataset. | |
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comsci.blog
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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 | |
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coornail.net
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| | | Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow. | ||
