Projects sharing features with Pytorch Exercises

  李航老师的《统计学习方法》和《机器学习方法》是机器学习领域的经典入门教材之一。本书分为监督学习、无监督学习和深度学习，全面系统地介绍了机器学习的主要方法。

A Mindmap summarising Deep Learning concepts, Architectures, and the Tensorflow library.

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

This is easy-to-follow Capsule Network tutorial with clean readable code: Capsule Network.ipynb

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

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

The Hitchiker's Guide to PyTorch

This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u

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

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

The PyTorch Tutorials repository is a collection of educational resources that provides step-by-step guidance on building, training, and deploying neural networks using the PyTorch framework. It covers the complete machine learning workflow, from data loading and model definition through optimization loops and model persistence, with dedicated guides for distributed training, model fine-tuning, and deployment. The tutorials offer practical demonstrations of adapting pre-trained models to new tasks through transfer learning, scaling training across multiple GPUs or machines using PyTorch's dis

This project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings. The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and

机器学习原理笔记整理. Gitbook地址https://shunliz.gitbooks.io/machine-learning/content/ 前半部分关注数学基础，机器学习和深度学习的理论部分，详尽的公式推导。 后半部分关注工程实践和理论应用部分

Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural networks. The project provides practical code for building sequence models and sequence-to-sequence architectures using the PyTorch framework. The repository covers the implementation of models for neural machine translation, character-level text generation, and text classification. It includes examples for transforming input sequences into output sequences for machine translation and synthesizing new text. The project also extends to sequence data prediction and time series analy