Computer Vision With PyTorch

This repository contains Jupyter notebooks demonstrating fundamental concepts in neural networks and computer vision using PyTorch.

Notebooks

• Auto_Gradient_Of_Tensor.ipynb: Demonstrates automatic gradient computation for tensors in PyTorch.

• Backpropagation.ipynb: Explains the backpropagation algorithm for computing gradients in neural networks.

• Building_a_neural_network_using_PyTorch_on_a_toy_dataset.ipynb: Builds a neural network using PyTorch on a simple toy dataset.

• Chain_Rule.ipynb: Demonstrates the chain rule in calculus, essential for backpropagation.

• Feed_Forward_Propagation.ipynb: Implements feed-forward propagation in a neural network, showing how inputs are processed through layers to produce outputs.

• Gradient_Descent.ipynb: Demonstrates gradient descent optimization algorithm used for training neural networks, including weight updates and loss minimization.

• Implementing_custom_loss_function.ipynb: Shows how to implement a custom loss function in PyTorch.

• Initialinzing_A_Tensor.ipynb: Covers different ways to initialize tensors in PyTorch.

• Learning_Rate.ipynb: Explores the impact of different learning rates on the training process of neural networks.

• Oeprations_On_Tensors.ipynb: Demonstrates various operations that can be performed on tensors in PyTorch.

• Specifying_batch_size_while_training_a_model.ipynb: Explains how to specify batch size when training a model in PyTorch.

Requirements

• Python 3.x
• NumPy
• Matplotlib
• PyTorch (if applicable in future notebooks)

Usage

Open the notebooks in Jupyter Lab or VS Code to run the code cells and see the visualizations.

References

• Modern Computer Vision with PyTorch - Official repository for the book "Modern Computer Vision with PyTorch" by Packt Publishing.

License

This project is for educational purposes.