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HandsOnDeepLearningWithPytorch

Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio

Chapters

  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production

Utilities

  • Visualization is handled by Netron -

    pip install netron

  • Environment is handled by Pipenv

Usage

  • Clone the repository

    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7

    conda env create -f environment.yml

  • CD to chapter directores and execute the models

About

Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch

Resources

Readme
MIT license
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GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
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HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
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0 watching
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0 forks
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, 'i');
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GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
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HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
0 watching
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0 forks
Report repository
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, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
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Repository files navigation
HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
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HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
Skip to content
BranchesTags
Repository files navigation
HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
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})();
GitHub - ericharper/HandsOnDeepLearningWithPytorch: Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch · GitHub
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HandsOnDeepLearningWithPytorch
Repository is arranged chapter wise and each folder includes the code used + the visualization of models use. Dataset used for the models are either available in the shared box folder or downloadable from the torch utility packages such as torchvision, torchtext or torchaudio
Chapters
  1. Introduction
  2. A Simple Neural Network
  3. Deep Learning work flow
  4. Computer Vision
  5. Sequential Data Processing
  6. Generative Networks
  7. Reinforcement Learning
  8. PyTorch In Production
Utilities
  • Visualization is handled by Netron -
    pip install netron

  • Environment is handled by Pipenv
Usage
  • Clone the repository
    git clone https://github.com/hhsecond/HandsOnDeepLearningWithPytorch.git && cd HandsOnDeepLearningWithPytorch

  • Install dependancies. HandsOnDeepLearningWithPytorch is using conda with python3.7
    conda env create -f environment.yml

  • CD to chapter directores and execute the models
About
Code snippets and applications explained in the book - HandsOnDeepLearningWithPytorch
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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