Apache CN deep learning translation set 20210125 update

Time:2021-6-3

Seven new tutorials have been added:

  • Pytorch official Chinese course 1.7

    • Learn pytorch

      • Pytorch deep learning: a 60 minute shock

        • tensor
        • torch.autogradA brief introduction to
        • neural network
        • Training classifier
      • Learn pytorch through examples

        • Warm up: numpy
        • Pytorch: tensor
        • Pytorch: tensor and autograd
        • Pytorch: define a new autograd function
        • PyTorch:nn
        • PyTorch:optim
        • Pytorch: Customnnmodular
        • Pytorch: control flow + weight sharing
      • torch.nnIn the end what is it?
      • Use tensorboard to visualize models, data and training
    • Picture / video

      • torchvisionObject detection tuning tutorial
      • Transfer learning course of computer vision
      • Countermeasure example generation
      • Dcgan tutorial
    • audio frequency

      • Audio I / O andtorchaudioPretreatment of water
      • usetorchaudioSpeech command recognition based on DSP
    • text

      • usenn.TransformerandtorchtextSequence to sequence modeling based on XML
      • Zero based NLP: use character level RNN classification names
      • Zero based NLP: use character level RNN to generate names
      • NLP from scratch: using sequence to sequence network and attention translation
      • usetorchtextText classification based on XML
      • torchtextLanguage translation
    • Reinforcement learning

      • Reinforcement learning (dqn) course
      • RL agent for training and playing Mario
    • Deploying pytorch model in production

      • Deploying pytorch in Python by using flash’s rest API
      • Introduction to torchscript
      • Loading torchscript model in C + +
      • Export the model from pytorch to onnx and run it using the onnx runtime (optional)
    • Front end API

      • A brief introduction to named tensors in pytorch (prototype)
      • The last memory format of the channel in pytorch (beta)
      • Using pytorch C + + front end
      • Custom C + + and CUDA extension
      • Extending torchscript with custom C + + operators
      • Extending torchscript with custom C + + classes
      • Dynamic parallelism in torchscript
      • Autograd in C + + front end
      • Registering scheduling operators in C + +
    • Model optimization

      • Analyze your pytorch module
      • Super parameter adjustment using ray tune
      • Model clipping tutorial
      • Dynamic quantization on LSTM word language model (beta)
      • Dynamic quantization on Bert (beta)
      • Static quantization using Eagle mode in pytorch (beta)
      • Quantitative transfer of computer vision (beta)
    • Parallel and distributed training

      • Overview of pytorch distributed
      • Single machine model parallel best practices
      • Introduction to distributed data parallelism
      • Using pytorch to write distributed application
      • Introduction to distributed RPC framework
      • Using distributed RPC framework to realize parameter server
      • Parallelization of distributed pipeline using RPC
      • Batch RPC processing using asynchronous execution
      • DistributeDataParallelCombined with distributed RPC framework
  • Pytorch Seminar on Artificial Intelligence

    • 0. Preface
    • 1、 Introduction to deep learning and pytorch
    • 2、 Building blocks of neural networks
    • 3、 Classification problems using DNN
    • 4、 Convolutional neural network
    • 5、 Style migration
    • 6、 Using RNN to analyze data sequence
    • 7、 Appendix
  • A practical guide to Python learning

    • 0. Preface
    • Part one: a brief introduction of a study

      • 1、 Introduction to a study
    • The second part: deep learning framework

      • 2、 Index based method
      • 3、 Model based method
      • 4、 Method based on Optimization
    • The third part: other methods and conclusions

      • 5、 Modeling method based on generation
      • 6、 Summary and other methods
  • A practical guide to Python natural language processing

    • 0. Preface
    • The first part: the main points of pytorch 1. X for NLP

      • 1、 The foundation of machine learning and deep learning
      • 2、 Introduction to pytorch 1. X for NLP
    • Part two: the basis of natural language processing

      • 3、 NLP and text embedding
      • 4、 Text preprocessing, stem extraction and word form reduction
    • The third part: the actual NLP application using pytorch 1. X

      • 5、 Recurrent neural network and emotion analysis
      • 6、 Convolutional neural network for text classification
      • 7、 Text translation using sequence to sequence neural networks
      • 8、 Construction of chat robot using attention based neural network
      • 9、 The road ahead
  • Pytorch basic knowledge of artificial intelligence

    • 0. Preface
    • 1、 Using pytorch, using tensor
    • 2、 Cooperation with neural network
    • 3、 Convolutional neural network for computer vision
    • 4、 Recurrent neural network for NLP
    • 5、 Transfer learning and tensorboard
    • 6、 Explore and generate confrontation network
    • 7、 Deep reinforcement learning
    • 8、 Producing AI model in pytorch
  • Pytorch deep learning practical guide

    • 0. Preface
    • 1、 Introduction to deep learning exercise and pytorch
    • 2、 Simple neural network
    • 3、 Workflow of deep learning
    • 4、 Computer vision
    • 5、 Sequence data processing
    • 6、 Generating network
    • 7、 Reinforcement learning
    • 8、 Pytorch in production
  • Tensorflow reinforcement learning

    • 0. Preface
    • 1、 Deep learning – Architecture and framework
    • 2、 Training reinforcement learning agents with openai gym
    • 3、 Markov decision process
    • 4、 Strategy gradient
    • 5、 Q learning and deep Q network
    • 6、 Asynchronous method
    • 7、 It’s all robots – real strategy games
    • 8、 Alphago – the best reinforcement learning
    • 9、 Reinforcement learning in automatic driving
    • 10、 Financial portfolio management
    • 11、 Reinforcement learning in Robotics
    • 12、 Deep reinforcement learning in advertising technology
    • 13、 Reinforcement learning in image processing
    • 14、 Deep reinforcement learning in NLP
    • 15、 Other topics of reinforcement learning

download

Docker

docker pull apachecn0/apachecn-dl-zh
docker run -tid -p <port>:80 apachecn0/apachecn-dl-zh
#Visit http://localhost :{port}

PYPI

pip install apachecn-dl-zh
apachecn-dl-zh <port>
#Visit http://localhost :{port}

NPM

npm install -g apachecn-dl-zh
apachecn-dl-zh <port>
#Visit http://localhost :{port}

Contribution Guide

This project needs proofreading, welcome to submit pull request.

Please be brave to translate and improve your translation. Although we pursue excellence, we don’t require you to be perfect, so please don’t worry about mistakes in Translation – in most cases, our server has recorded all translations, so you don’t have to worry about irreparable damage due to your mistakes( (adapted from Wikipedia)