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 ben.bolte.cc
A Neural Network in 28 Lines of Theano
| | www.khanna.law
Structuring Deep Learning Projects
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| | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now?
| | demofox.org
Neural Network Visualization 2D
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| | [AI summary] A blog post demo showcasing the visualization of neural networks using GLSL shaders to represent output values with distinct colors based on their magnitude.
| | blog.vstelt.dev
Writing a Neural Network from Scratch in Rust
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| | [AI summary] The article explains the process of building a neural network from scratch in Rust, covering forward and backward propagation, matrix operations, and code implementation.
| | matt.might.net
Hello, Perceptron: An introduction to artificial neural networks
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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 ...