🔥 Deep Learning with PyTorch

Welcome to the Deep Learning with PyTorch repository!
This repo is your one-stop guide to mastering PyTorch — starting from the fundamentals to building powerful models for computer vision tasks. Whether you're a beginner or revising your knowledge, these well-documented Jupyter notebooks will help you learn PyTorch efficiently.

📁 Repository Structure

Deep-Learning-with-PyTorch/
├── colab_notebooks/                            # All PyTorch tutorial notebooks
│   ├── 1_fundamentals_of_torch.ipynb           # Tensors, autograd, basic ops
│   ├── 2_workflow_of_torch.ipynb               # Model → Loss → Optimizer → Training loop
│   ├── 3_classification_with_pytorch.ipynb     # Binary classification example
│   ├── 4_multiclass_classification_with_torch.ipynb  # Multi-class classification
│   └── 5_computer_vision_with_torch.ipynb      # CNN + image classification
├── README.md                                   # Project overview and guide
└── LICENSE                                     # MIT License (or your license of choice)

🚀 Notebooks Overview

Notebook	Description
1. Fundamentals of Torch	Learn tensors, autograd, and basic operations with PyTorch.
2. Workflow of Torch	Understand the typical deep learning pipeline using PyTorch.
3. Classification with PyTorch	Implement a simple binary classification model from scratch.
4. Multiclass Classification	Build and train a model for multiclass classification problems.
5. Computer Vision with Torch	Use PyTorch for image classification tasks using CNNs.

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▶️ Run Tutorials on Google Colab

Click any of the notebooks below to open them directly in Google Colab:

🔹 01 - Torch Fundamentals

🔹 02 - Workflow of Torch

🔹 03 - Classification with PyTorch

🔹 04 - Multiclass Classification

🔹 05 - Computer Vision with Torch

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✨ What You'll Learn

• How to use tensors and perform operations in PyTorch
• The standard deep learning workflow in PyTorch
• Building classification models (binary and multiclass)
• How to work with image data and convolutional neural networks (CNNs)
• Training, evaluation, and performance metrics

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🧠 Ideal For

• Beginners exploring deep learning
• Intermediate learners switching from TensorFlow to PyTorch
• Students preparing for ML/DL interviews or projects
• Anyone wanting hands-on practice with PyTorch

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🛠 Tech Stack

• Python 3.x
• PyTorch
• Jupyter Notebooks
• Google Colab (optional)

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📌 How to Use

1. Clone this repository:

   git clone https://github.com/GANESH9124/Deep-Learning-with-PyTorch.git
   cd Deep-Learning-with-PyTorch/colab_notebooks
   
   
   

📃 License

This repository is licensed under the MIT License. Feel free to fork, learn, modify, and share!