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mathspp.com | ||
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www.hamza.se
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| | | | | A walkthrough of implementing a neural network from scratch in Python, exploring what makes these seemingly complex systems actually quite straightforward. | |
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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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www.jeremymorgan.com
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| | | | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | |
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matt.might.net
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| | | [AI summary] This text explains how a single perceptron can learn basic Boolean functions like AND, OR, and NOT, but fails to learn the non-linearly separable XOR function. This limitation led to the development of modern artificial neural networks (ANNs). The transition from single perceptrons to ANNs involves three key changes: 1) Adding multiple layers of perceptrons to create Multilayer Perceptron (MLP) networks, enabling modeling of complex non-linear relationships. 2) Introducing non-linear activation functions like sigmoid, tanh, and ReLU to allow networks to learn non-linear functions. 3) Implementing backpropagation and gradient descent algorithms for efficient training of multilayer networks. These changes allow ANNs to overcome the limitations of ... | ||
