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Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of neural networks from scratch. It serves as a programming tutorial and implementation guide for understanding the internal mechanics of deep learning. The project focuses on building various network architectures, including convolutional, recurrent, and long short-term memory networks. It provides step-by-step implementations of fundamental mechanisms such as forward propagation, backpropagation, and gradient descent. The material covers a broad range of deep learning capabilit
This repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including
Pumpkin-book is an open-source educational textbook that provides annotated study materials and mathematical derivations for foundational machine learning concepts. It functions as a technical documentation archive, breaking down dense academic literature into accessible, plain-language notes designed to support self-paced learning. The project distinguishes itself through a collaborative knowledge curation model, where the curriculum is managed via a version-controlled system. This workflow relies on community-driven updates and peer review to refine explanations and ensure the accuracy of t
This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement
由于17版李宏毅机器学习课程笔记《leeml-notes》内容已稍显陈旧，遂基于21版李宏毅机器学习课程重新撰写，更名为《leedl-tutorial》，GitHub仓库地址为：https://github.com/datawhalechina/leedl-tutorial
The main features of datawhalechina/leeml-notes are: Learning and Reference, Learning Resources.
Open-source alternatives to datawhalechina/leeml-notes include: oxford-cs-deepnlp-2017/lectures — This repository is a deep learning for natural language processing course and curriculum. It provides educational… yunjey/pytorch-tutorial — This project is a collection of educational examples and code for implementing deep learning architectures using the… datawhalechina/pumpkin-book — Pumpkin-book is an open-source educational textbook that provides annotated study materials and mathematical… iamtrask/grokking-deep-learning — Grokking-Deep-Learning is a collection of educational resources and courseware designed to teach the construction of… pytorch/examples — This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning… atcold/pytorch-deep-learning-minicourse — This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep…