Data science teaching materials
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Updated
Mar 5, 2024 - Jupyter Notebook
Data science teaching materials
Deep learning library in python from scratch
A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs
An Open Convolutional Neural Network Framework in C++ From Scratch
A set of Jupyter notebooks implementing simple neural networks described in Michael Nielsen's book.
JABACAT-created machine learning library from scratch.
Neural Networks Fundamentals with Python – implementing neural networks from scratch
XOR gate which predicts the output using Neural Network 🔥
Matrix-Vector Library Designed for Neural Network Construction. cuda (gpu) support, openmp (multithreaded cpu) support, partial support of BLAS, expression template based implementation PTX code generation identical to hand written kernels, and support for auto-differentiation
Neural nets for high accuracy multivariable nonlinear regression.
🤖 A TypeScript version of karpathy/micrograd — a tiny scalar-valued autograd engine and a neural net on top of it
Multilayer Perceptron from scratch in python
Neural Network with VHDL and matlab
A library of neural network implementations written using just Python and NumPy, as a resource for learning.
Artificial Neural Network (MLP) and Deep Q-Learning Implementation from scratch, only using numpy.
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
To understand neural networks thoroughly I implemented them from scratch in C++. This is the source code for the same.
Pure Python Simple Neural Network (SNN) library
Neural Networks and optimizers from scratch in NumPy, featuring newer optimizers such as DemonAdam or QHAdam.
Let's build Neural Networks from scratch.
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