Introduction
The deep learning frameworks currently being used by researchers are different, including TensorFlow, PyTorch, Keras, etc. These deep learning frameworks are applied in fields such as computer vision, speech recognition, natural language processing and bioinformatics, and have achieved excellent results. Among them, PyTorch is a rare simple, elegant, efficient and fast framework. Among the current open source frameworks, no framework can surpass PyTorch at the same time in terms of flexibility, ease of use and speed.
This document is positioned as a beginner tutorial for PyTorch, mainly aimed at students or deep learning enthusiasts who want to learn PyTorch. Through the study of tutorials, you can achieve deep learning with zero foundation, reduce the difficulty of self-study, and quickly learn PyTorch.
The official tutorial includes introduction to PyTorch and installation tutorials; a 60-minute quick start tutorial, which can quickly complete a classifier model from the novice stage; a commonly used model for computer vision, which is convenient for adjustment based on your own data, and no longer requires writing from scratch; natural language processing models, chatbots, text generation and other vivid and interesting projects.
all in all:
- If you want to learn more about PyTorch, you can see the introduction section.
- If you want to get started quickly with PyTorch, you can watch 60 minutes to get started.
- If you want to solve computer vision problems, you can look at the computer vision section.
- If you want to solve natural language processing problems, you can see the NLP section.
- There are also some contents of reinforcement learning and generative adversarial networks.
PyTorch official document video version is available online on B station
https://www.bilibili.com/video/BV1GS4y1F71Q/
Collection https://github.com/fendouai/PyTorchVideo
Author: PyTorchChina PyTorch Translation Team: News & PanChuang
Original text: https://pytorch.org/tutorials/
Table of contents
Chapter 1: Introduction and Download of PyTorch
1. Introduction to PyTorch
2.PyTorch environment construction
Chapter 2: Getting Started with PyTorch: 60min
1. Getting started with PyTorch
2.PyTorch automatic differentiation
3.PyTorch neural network
4.PyTorch Image Classifier
5.PyTorch data parallel processing
Chapter 3: Introduction to PyTorch
1. Data loading and processing
2.PyTorch test
3. Transfer Learning
4. Deployment of seq2seq model for hybrid front-end
5. Save and load the model
Chapter 4: PyTorch's Images
1. Fine-tune the object detection model based on torchvision 0.3
2. Fine-tune the TorchVision model
3. Space converter network
4. Use PyTorch for Neural-Transfer
5. Generate confrontation examples
6. Use ONNX to transfer the model to Caffe2 and mobile
Chapter 5: PyTorch's text
1. Chatbot tutorial
2. Use character-level RNN to generate names
3. Use character-level RNN for name classification
4. Use Pytorch in Deep Learning and NLP
5. Use Sequence2Sequence network and attention for translation
Chapter 6: PyTorch's Generation and Adversarial Network
1. Generative Adversarial Networks
Chapter 7: PyTorch’s Reinforcement Learning
1. Reinforcement Learning (DQN)
Tutorial recommendations
- PyTorch Introduction Tutorial
http://pytorchchina.com
- Panchuang AI chatbot, intelligent customer service:
http://www.panchangai.com/
- Panchuang Tutorial Website, TensorFlow, Pytorch, Keras:
http://panchang.net/
- Magic Picture Internet Knowledge Graph Recommendation System:
http://motuhulian.com
Due to the limited level of translator, if there are any omissions, please submit a PR.
