Welcome to my repository for learning PyTorch, a powerful deep learning framework! This repository documents my journey as I explore PyTorch from the basics to more advanced implementations and tutorials.

The Basics folder contains foundational materials and introductory resources to help you get started with PyTorch. Topics covered include:

• Installation and setup of PyTorch
• Basic tensor operations and manipulations
• Understanding PyTorch's autograd mechanism for automatic differentiation
• Building simple neural networks using PyTorch's nn module

In the From Scratch section, I delve into building neural networks and deep learning models from scratch using PyTorch. This includes:

• Implementing basic neural network architectures such as feedforward networks and convolutional neural networks (CNNs) without using PyTorch's built-in layers and modules
• Understanding the underlying mathematics and principles behind deep learning models
• Implementing backpropagation and gradient descent algorithms from scratch

The Implementations folder have more advanced implementations of deep learning models and algorithms using PyTorch. This section includes:

• Implementations of state-of-the-art deep learning architectures for tasks such as image classification, object detection, and image segmentation.

In the PyTorch tutorials section, you'll find tutorials and guides to help you deepen your understanding of PyTorch and its capabilities. This includes:

• Step-by-step tutorials covering various aspects of PyTorch, from basic operations to advanced topics

Please note that this section will be updated over time as I delve deeper into the world of deep learning and implement more algorithms and concepts from scratch using PyTorch.

Contributions to this repository are welcome! If you have suggestions for improving existing materials, adding new resources, or fixing issues.

Feel free to connect with me on LinkedIn for further discussion.