Simple neural network library for understanding the basic behind them. This library was made following both the book Nature of Code (chapter 10) and the Youtube's playlist with the same name made by Daniel Shiffman
- Youtube
- 10: Neural Networks - The Nature of Code
- Essence of linear algebra
- Neural Networks
- Gradient descent, how neural networks learn
- Linear Regression with Gradient Descent
- Books
- The Nature of Code book
- Make Your Own Neural Network
- Reading
- https://en.wikipedia.org/wiki/Perceptron
- https://en.wikipedia.org/wiki/Hadamard_product_(matrices)
- https://en.wikipedia.org/wiki/Feedforward_neural_network
- https://en.wikipedia.org/wiki/Sigmoid_function
- https://en.wikipedia.org/wiki/Stochastic_gradient_descent
- https://en.wikipedia.org/wiki/Backpropagation
- https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/exp
- https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c
- https://ml4a.github.io/ml4a/neural_networks/
- https://ml4a.github.io/ml4a/looking_inside_neural_nets/
- https://ml4a.github.io/ml4a/how_neural_networks_are_trained/
- Repos
- https://github.com/shiffman/Neural-Network-p5
- https://github.com/CodingTrain/Toy-Neural-Network-JS/
- https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning/tree/master/week4-neural-networks
- https://github.com/ml4a http://matrixmultiplication.xyz/
- Libraries
- https://mathjs.org/
- http://gpu.rocks/
- https://www.tensorflow.org/js/
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