README.md

# PyTorch Neural Networks

<!--
Project: PyTorch Neural Networks
Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
Description: Building neural networks with PyTorch including dynamic computation graphs, automatic differentiation, and model training.
-->

Building neural networks with PyTorch including dynamic computation graphs, automatic differentiation, and model training.

## Description

This project demonstrates PyTorch, Facebook's deep learning framework with dynamic computation graphs. It covers tensor operations, automatic differentiation, neural network construction, training loops, and advanced features. Perfect for research and production deep learning.

## Features

- Dynamic computation graphs
- Automatic differentiation
- Neural network modules (Basic NN, CNN, RNN)
- Training and optimization
- Model deployment
- **Advanced Training Features:**
- Early stopping
- Model checkpointing
- Learning rate scheduling
- Gradient clipping
- Mixed precision training
- Distributed training support
- **Evaluation Metrics:**
- Confusion matrix visualization
- Classification reports
- Accuracy metrics
- **Data Augmentation:**
- Image augmentation
- Sequence augmentation
- MixUp augmentation
- **Transfer Learning:**
- Pre-trained models (ResNet, VGG, DenseNet)
- Fine-tuning utilities
- **Hyperparameter Tuning:**
- Grid search
- Random search
- **TensorBoard Integration:**
- Training visualization
- Metric logging
- Model graph visualization

## Technologies

- Python
- PyTorch
- NumPy
- Matplotlib
- Jupyter Notebook

## Installation

```bash
pip install -r requirements.txt
```

## Project Structure

```
pytorch-neuralnetworks/
├── README.md
├── requirements.txt
├── main.py
├── example.py
├── deploy.py
├── .gitignore
├── models/
│ ├── __init__.py
│ ├── basic_nn.py
│ ├── cnn.py
│ ├── rnn.py
│ └── advanced.py
├── training/
│ ├── __init__.py
│ ├── trainer.py
│ ├── advanced_trainer.py
│ ├── callbacks.py
│ ├── metrics.py
│ └── utils.py
├── data/
│ ├── __init__.py
│ ├── augmentation.py
│ └── datasets.py
├── utils/
│ ├── __init__.py
│ ├── hyperparameter_tuning.py
│ └── tensorboard_logger.py
├── models/
│ ├── __init__.py
│ ├── basic_nn.py
│ ├── cnn.py
│ ├── rnn.py
│ ├── advanced.py
│ └── transfer_learning.py
├── examples/
│ ├── advanced_features_example.py
│ ├── transfer_learning_example.py
│ └── hyperparameter_tuning_example.py
├── notebooks/
│ ├── 01_tensor_operations.ipynb
│ ├── 02_automatic_differentiation.ipynb
│ ├── 03_basic_neural_network.ipynb
│ ├── 04_cnn_example.ipynb
│ ├── 05_rnn_example.ipynb
│ └── 06_model_deployment.ipynb
├── data/
│ └── .gitkeep
└── saved_models/
└── .gitkeep
```

## Usage

### Basic Neural Network

```python
python main.py --model basic --epochs 10
```

### CNN Example

```python
python main.py --model cnn --epochs 20
```

### RNN Example

```python
python main.py --model rnn --epochs 15
```

## Quick Start Example

Run the quick start example to see a complete training workflow:

```bash
python example.py
```

## Jupyter Notebooks

Launch Jupyter Notebook to explore interactive examples:

```bash
jupyter notebook notebooks/
```

## Model Deployment

Deploy a trained model for inference:

```bash
python deploy.py --model_type basic --model_path saved_models/model.pth
```

## Advanced Features Examples

### Advanced Training Features

```bash
python examples/advanced_features_example.py
```

This demonstrates:
- Early stopping
- Model checkpointing
- Learning rate scheduling
- Gradient clipping
- TensorBoard logging

### Transfer Learning

```bash
python examples/transfer_learning_example.py
```

This demonstrates:
- Using pre-trained models
- Fine-tuning strategies
- Freezing/unfreezing layers

### Hyperparameter Tuning

```bash
python examples/hyperparameter_tuning_example.py
```

This demonstrates:
- Grid search
- Random search
- Parameter optimization

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

Copyright (c) 2024 RSK World

This project is provided by RSK World (https://rskworld.in) for educational purposes.

## Contact

- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277