🔥 PyTorch Workshop

A comprehensive PyTorch workshop covering the fundamentals and advanced techniques of deep learning.

📖 Table of Contents

📖 Main Notebooks

1. Introduction to Tensors
2. Gradient and Autograd
3. Perceptron and AdaLiNe
4. Regression Models
5. Multi-Layer Perceptrons
6. Radial Basis Function Networks
7. Convolutional Neural Networks
8. Feature Extraction
9. Transfer Learning
10. Fine-Tuning Models
11. Recurrent Neural Networks

📖 Utilities

A collection of concepts and tools utilized in the main notebooks for training models, ...

• Activation Functions
• Checkpoints
• Working with Datasets
• Parameters vs. Hyperparameters
• Loss Functions
• Metrics
• Model Creation
• Normalization Techniques
• Optimizers
• Vision Transforms
• Word Embeddings

📖 Models

• CNN Architectures
  1. LeNet-5 Architecture
  2. AlexNet Architecture
  3. VGGNet Architecture
  4. GoogLeNet Architecture
  5. Xception Architecture
  6. ResNet Architecture
  7. DenseNet Architecture
  8. EfficientNet Architecture

📖 Projects

Implementation details are provided in the README files within the parent directories.

1. MNIST Classification
   • Implementation 1
2. CIFAR-10 Classification
   • Implementation 1

📋 Prerequisites

• 👨‍💻 Programming Fundamentals
  • Proficiency in Python (data types, control structures, functions, classes, etc.).
    • My Python Workshop: github.com/mr-pylin/python-workshop
  • Experience with libraries like NumPy, Pandas and Matplotlib.
    • My NumPy Workshop: github.com/mr-pylin/numpy-workshop
    • My Pandas Workshop: Coming Soon
    • My Data Visualization Workshop: github.com/mr-pylin/data-visualization-workshop
• 🔣 Mathematics for Machine Learning
  • 🔲 Linear Algebra: Vectors, matrices, matrix operations.
    • Linear Algebra Review and Reference written by Zico Kolter.
    • Notes on Linear Algebra written by Peter J. Cameron.
    • MATH 233 - Linear Algebra I Lecture Notes written by Cesar O. Aguilar.
  • 📈 Calculus: Derivatives, gradients, partial derivatives, chain rule (for backpropagation).
    • Lecture notes on advanced gradient descent written by Cl´ement W. Royer.
    • MATH 221 – CALCULUS LECTURE NOTES VERSION 2.0 written by Sigurd Angenent.
    • Calculus written by Gilbert Strang.
  • 🎲 Probability & Statistics: Probability distributions, mean/variance, etc.
    • MATH1024: Introduction to Probability and Statistics written by Sujit Sahu.

⚙️ Setup

This project requires Python v3.10 or higher. It was developed and tested using Python v3.12.8. If you encounter issues running the specified version of dependencies, consider using this version of Python.

📝 List of Dependencies

📦 Install Dependencies

📦 Method 1: Poetry (Recommended ✅)

Use Poetry for dependency management. It handles dependencies, virtual environments, and locking versions more efficiently than pip.
To install exact dependency versions specified in poetry.lock for consistent environments without installing the current project as a package:

poetry install --no-root

📦 Method 2: Pip

Install all dependencies listed in requirements.txt using pip:

pip install -r requirements.txt

🛠️ Usage Instructions

1. Open the root folder with VS Code (Ctrl/Cmd + K followed by Ctrl/Cmd + O).
2. Open .ipynb files using the Jupyter extension integrated with VS Code.
3. Select the correct Python kernel and virtual environment where the dependencies were installed.
4. Allow VS Code to install any recommended dependencies for working with Jupyter Notebooks.

✍️ Notes:

• It is highly recommended to stick with the exact dependency versions specified in poetry.lock or requirements.txt rather than using the latest package versions. The repository has been tested on these versions to ensure compatibility and stability.
• This repository is actively maintained, and dependencies are updated regularly to the latest stable versions.
• The table of contents embedded in the notebooks may not function correctly on GitHub.
• For an improved experience, open the notebooks locally or view them via nbviewer.

🔗 Useful Links

PyTorch

• Source Code:
  • Over 3500 contributors are currently working on PyTorch.
  • Link: github.com/pytorch/pytorch
• Website:
  • The official website for PyTorch, offering comprehensive documentation, tutorials, and resources for deep learning and machine learning with PyTorch.
  • Link: pytorch.org
• Pytorch Documentations:
  • Detailed and comprehensive documentation for all PyTorch features and functionalities, including tutorials and guides to help you get started and master PyTorch.
  • Link: pytorch.org/docs/stable/index.html
• TorchVision Documentations:
  • The torchvision package [part of the PyTorch] consists of popular datasets, model architectures, and common image transformations for computer vision.
  • Link: pytorch.org/vision/stable/index.html
• TorchAudio Documentation:
  • The torchaudio package [part of the PyTorch] consists of audio I/O and signal processing functionalities, enabling efficient loading, transforming, and manipulating audio.
  • Link: pytorch.org/audio/stable/index.html

NumPy

• A fundamental package for scientific computing in Python, providing support for arrays, matrices, and a large collection of mathematical functions.
• Official site: numpy.org

Pandas

• A powerful, open-source data analysis and manipulation library for Python.
• Pandas is built on top of NumPy.
• Official site: pandas.pydata.org

Data Visualization

• A comprehensive collection of Python libraries for creating static, animated, and interactive visualizations: Matplotlib, Seaborn, and Plotly.
• Official sites: matplotlib.org | seaborn.pydata.org | plotly.com

🔍 Find Me

Any mistakes, suggestions, or contributions? Feel free to reach out to me at:

• 📍linktr.ee/mr_pylin

I look forward to connecting with you! 🏃‍♂️

📄 License

This project is licensed under the Apache License 2.0.
You are free to use, modify, and distribute this code, but you must include copies of both the LICENSE and NOTICE files in any distribution of your work.

©️ Copyright Information

• Original Images:
  • The images located in the ./assets/images/original/ folder are licensed under the CC BY-ND 4.0.
  • Note: This license restricts derivative works, meaning you may share these images but cannot modify them.
• Third-Party Assets:
  • Additional images located in ./assets/images/third_party/ are used with permission or according to their original licenses.
  • Attributions and references to original sources are included in the code where these images are used.