/ Machine Learning

A set of jupyter notebooks on pytorch functions with examples

A set of jupyter notebooks on pytorch functions with examples

Pytorch_Tutorial

A set of jupyter notebooks on pytorch functions with examples.

Pytorch_Tutorial

  • A) RoadMap 1 - Torch Main 1 - Basic Tensor functions.ipynb
  • B) RoadMap 2 - Torch Main2 - Mathematical Operators.ipynb
  • C) RoadMap 3 - Torch Main 3 - Linear Algebraic Operations.ipynb
  • D) RoadMap 4 - Data 1 - Loader base codes.ipynb
  • E) RoadMap 5 - Data 2 - Transformations (General).ipynb
  • F) RoadMap 6 - Data 3 - Loader example codes.ipynb
  • G) RoadMap 7 - Torch NN 1 - Convolution, Pooling and Padding Layers.ipynb
  • H) RoadMap 8 - Torch NN 2 - Activation Layers.ipynb
  • I) RoadMap 9 - Torch NN 3 - Other Layers.ipynb
  • J) RoadMap 10 - Torch NN 4 - Initializers.ipynb
  • K) RoadMap 11 - Torch NN 5 - Loss Functions.ipynb
  • L) RoadMap 12 - Torch NN 6 - Base Modules.ipynb
  • M) RoadMap 13 - Torch NN 7 - Optimizers and learning rate adjustment.ipynb
  • N) RoadMap 14 - Classification 1 - Pytorch model zoo.ipynb
  • O) RoadMap 15 - Classification 2 - Training & Validating [Custom CNN, Public Dataset].ipynb
  • P) RoadMap 16 - Classification 3 - Training & Validating [Custom CNN, Custom Dataset].ipynb
  • Q) RoadMap 17 - Classification 4 - Transfer learning [Custom Dataset, Learning Rate Scheduler, Model saver].ipynb
  • R) RoadMap 18 - Appendix 1 - Replicating Classification 4 with Monk.ipynb
  • S) RoadMap 19 - Appendix 2 - Fashion Classification with Monk.ipynb
  • T) RoadMap 20 - Appendix 3 - Indoor Scene Classification with Monk.ipynb
  • U) RoadMap 21 - Appendix 4 - American Sign Language Classification with Monk.ipynb
  • V) RoadMap 23 - Appendix 5 - Plant Disease Classification with Monk.ipynb
  • W) RoadMap 24 - Appendix 6 - Food Classification with Monk.ipynb

Installation

pip install -r requirements.txt

Author

Tessellate Imaging - https://www.tessellateimaging.com/

Check out Monk AI - (https://github.com/Tessellate-Imaging/monk_v1)

Monk features
    - low-code
    - unified wrapper over major deep learning framework - keras, pytorch, gluoncv
    - syntax invariant wrapper

Enables developers
    - to create, manage and version control deep learning experiments
    - to compare experiments across training metrics
    - to quickly find best hyper-parameters

To contribute to Monk AI or Pytorch_Tutoral repository raise an issue in the git-repo or dm us on linkedin

GitHub