PyTorch: From Basics to Scratch

Welcome to the PyTorch: From Basics to Scratch repository! This repository is designed to help learners understand PyTorch fundamentals and deep learning concepts step by step, progressing from beginner to advanced topics.

📌 Table of Contents

Lesson	Title	Description	Why It's Useful
L1	Introduction	Overview of PyTorch, installation, and basic concepts.	Provides a strong foundation for deep learning with PyTorch.
L2	Starting with Tensors	Introduction to tensors, basic operations, and manipulation.	Tensors are the core data structure in PyTorch; mastering them is essential.
L3	Autograd	Understanding automatic differentiation in PyTorch.	Essential for backpropagation and optimizing neural networks.
L4	Training Pipeline	Building a simple training pipeline for neural networks.	Helps in structuring and automating the training process.
L5	NN Module	Exploring PyTorch's torch.nn module for building neural networks.	Simplifies model building and allows modular implementation.
L6	DataLoader Class	Using PyTorch's DataLoader to efficiently load and preprocess data.	Essential for handling large datasets and training efficiently.
L7	Implementing First ANN	Implementing a simple Artificial Neural Network (ANN) from scratch.	Provides hands-on experience in designing and training models.
L8	Training Using GPUs	Leveraging GPU acceleration for training deep learning models.	Enhances performance by utilizing CUDA-enabled GPUs.
L9	ANN Optimization	Techniques to optimize ANN training, including loss functions and optimizers.	Improves training efficiency and accuracy of deep learning models.
L10	Hyperparameter Tuning	Techniques to fine-tune hyperparameters for better model performance.	Helps in achieving optimal results by tuning parameters like learning rate, batch size, etc.
L11	Computer Vision (CV) Introduction	Basics of deep learning in computer vision applications.	Useful for building CV-based models like image classification.
L12	CNN in PyTorch	Implementing Convolutional Neural Networks (CNNs) in PyTorch.	CNNs are widely used in image recognition and classification tasks.
L13	First Paper Implementation	Implementing a research paper using PyTorch.	Helps in understanding how to translate research into working code.
L14	Continual Learning (Part 1)	Understanding the concept of continual learning in deep learning.	Enables models to learn incrementally from new data.
L14	Continual Learning (Part 2)	Advanced continual learning techniques and strategies.	Essential for long-term model adaptability and avoiding catastrophic forgetting.
L15	Transfer Learning	Applying pre-trained models to new tasks.	Saves training time and improves performance on smaller datasets.
L16	Data Augmentation	Techniques to augment data for improving model generalization.	Helps prevent overfitting by artificially increasing dataset size.
L17	Data Manipulation (Part 1)	Working with datasets, transformations, and preprocessing.	Essential for preparing data before training models.
L17	Data Manipulation (Part 2)	Advanced data preprocessing and manipulation techniques.	Improves data handling for complex machine learning tasks.

🚀 Getting Started

1. Clone the repository

   git clone https://github.com/Shantnu-singh/PyTorch---Basic-to-Advance 

2. Set up a virtual environment (optional but recommended)

   python -m venv venv
   venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run Jupyter Notebook

   jupyter notebook

🤝 Contributing

Contributions are welcome! Feel free to open issues, suggest improvements, or submit pull requests here.