Welcome to Learn PyTorch: A Mathematical Perspective! This tutorial series aims to teach you the fundamentals of PyTorch through a mix of math and code, making complex topics accessible for beginners. We're building this project to be a collaborative space where contributions from different perspectives enhance the content!
This repository contains Jupyter notebooks, tutorials, and code examples that build a strong foundation in PyTorch while covering the essential math behind neural networks. Whether you’re just starting out or advancing your skills, this series will take you from the basics to the frontier of PyTorch.
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Introduction to PyTorch & Setup
- Installing PyTorch
- Working with Tensors
- Basic tensor operations and reshaping
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Math Foundations of Machine Learning
- Essential math for ML: vectors, matrices, and tensors
- Gradients, chain rule, and backpropagation
- Math reasoning for neural networks
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Building Neural Networks with PyTorch
- Layers, activation functions, and model structures
- Coding neural networks from scratch
- Detailed optimizer and loss function breakdowns
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Convolutional Neural Networks (CNNs)
- Convolutions and pooling explained
- Building and training CNNs
- Visualizing feature maps and filters
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Recurrent Neural Networks (RNNs) & LSTMs
- Time-series data and sequence modeling
- Building and training RNNs/LSTMs
- Applications to NLP with text data
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Advanced Topics
- Transfer Learning and fine-tuning
- Deploying PyTorch models
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Math Deep Dive
- In-depth on stochastic gradient descent (SGD)
- Vanishing and Exploding Gradients
- Regularization techniques and optimizers
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Project Showcase
- Hands-on projects to apply your skills
- Options for beginner and advanced levels
To get the most out of this tutorial series, it’s best to have:
- Basic Python knowledge (functions, loops, etc.)
- High school math level (especially in linear algebra and calculus basics)
No prior deep learning experience is required!
To get started, clone this repository and install the required libraries:
# Clone the repository
git clone https://github.com/username/PyTorch-Math-Tutorial.git
# Change directory into the repo
cd PyTorch-Math-Tutorial
# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtEach module contains Jupyter notebooks and scripts. It’s recommended to start with Module 1 and progress through the tutorials sequentially.
# To run a specific module
jupyter notebook modules/01-intro-to-pytorch/01_basics.ipynbBy the end of this series, you’ll:
- Understand key mathematical concepts in machine learning.
- Build neural networks from scratch in PyTorch.
- Explore complex architectures like CNNs and RNNs.
- Optimize models using advanced techniques.
- Deploy models for real-world applications.
- Step-by-step explanations of each concept
- Math-driven insights for beginners
- Hands-on code exercises and projects
- Clear and detailed comments in the code
- Interactive math visualizations to illustrate concepts
We’re looking for contributors to make this project a rich learning resource. Here are some areas where you can get involved:
- Content creation: Add modules, expand sections, or suggest real-world applications.
- Visual aids: Interactive graphs, visualizations, or mathematical diagrams are a huge plus.
- Code contributions: Improvements to code structure, optimizations, and new examples.
- Review and feedback: Review tutorials, propose enhancements, or suggest alternative explanations.
Check out our CONTRIBUTING.md for guidelines on how to get involved!
For help, feedback, or just to chat:
- Open an issue: Found a bug? Let us know!
- Join the discussions: Connect with other learners.
- Community chat: Coming soon!
This project is maintained by [Your Name] and is licensed under the MIT License. Feel free to use, modify, and share!
Happy learning, and enjoy your PyTorch journey from a mathematical perspective!