Implementing a Neural Network from Scratch
Die Hauptfunktionen von dennybritz/nn-from-scratch sind: Machine Learning and AI, Neural Network Architectures.
Open-Source-Alternativen zu dennybritz/nn-from-scratch sind unter anderem: dennybritz/rnn-tutorial-rnnlm — Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano. dennybritz/nn-theano — Speed up your Neural Network with Theano and the GPU. blealtan/efficient-kan — This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network… microsoft/ai-edu — ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset… pageman/sutskever-30-implementations — This project is a collection of deep learning research implementations and a reproduction kit designed to translate…
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano
This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning. The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions. The framework covers a range of capabilities including grid-based funct
SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The