Three-layer neural network classifier built from scratch.
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Updated
Sep 14, 2018 - Python
Three-layer neural network classifier built from scratch.
The code repository for my rouse research project, addressing the question: "How effective are machine learning algorithms compared with traditional analytical techniques, with respect to playing abstract games?"
Sudoku Solver using a constraint satisfaction approach based on constraint propagation and backtracking and another one based on Relaxation Labeling
The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.
the implementation of a multilayer perceptron
Understanding neural network libraries and the automatic gradient computations (autograd) in the backward pass
Implementing backpropagation for training simple neural networks.
A neural network to predict the category of the item in an image. Trained using the Fashion-MNIST dataset.
Solving Game of Life with Machine Learning
A deep neural network library built from scratch, implementing the famous backpropagation algorithm
Implementation of neural networks and backpropagation from scratch
NumPy Feedforward Neural Network
Tensorflow Simplified: Linear and Sigmoid Layers, Forward and Back Prop, Stochastic Gradient Descent
final versions of projects from TJHSST AI class 2018-19
Minimal implementation of backprop for multilayer perceptron.
A vanilla implementation of the back-propagation algorithm.
Learn to combine evolution and backpropagation 🚲
A feed forward neural network from scratch, that descicdes weather a point lies within the unit circle or not
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