TensorFlow Deep Learning

Deep learning with TensorFlow including neural networks, CNNs, RNNs, and building custom models for various applications.

Description

This project provides a comprehensive guide to TensorFlow, Google's deep learning framework. It covers neural network construction, convolutional neural networks (CNNs), recurrent neural networks (RNNs), custom layers, model training, and deployment. Perfect for building deep learning applications.

Features

Core Deep Learning Models

• Neural Networks: Feedforward networks, deep networks with batch normalization
• CNNs: Simple CNNs, deep CNNs, ResNet-style architectures
• RNNs: Simple RNN, LSTM, GRU, Bidirectional LSTM, Sequence-to-sequence models
• Transformers: Multi-head attention, encoder-decoder architectures
• Transfer Learning: Pre-trained models (VGG16, ResNet50, MobileNet, InceptionV3, etc.)
• GANs: Generative Adversarial Networks (DCGAN implementation)
• Autoencoders: Simple, Convolutional, and Variational Autoencoders

Advanced Features

• Custom Layers: Custom dense, attention, and residual layers
• Model Training: Advanced training techniques, callbacks, data augmentation, mixed precision
• Model Evaluation: Comprehensive metrics, confusion matrices, ROC curves
• Data Preprocessing: Image, text, and tabular data preprocessing pipelines
• Visualization: Training history, model architecture, feature importance, layer activations
• Model Deployment: SavedModel, H5, TFLite, TensorFlow.js, REST API
• Docker Support: Containerized deployment with Docker and Docker Compose

Technologies

• Deep Learning: TensorFlow, Keras
• Data Processing: NumPy, Pandas, Scikit-learn
• Visualization: Matplotlib, Seaborn
• Development: Jupyter Notebook, Python 3.8+
• Deployment: Flask, Docker, TensorFlow Serving
• Utilities: Pillow, TensorFlow.js

Project Structure

tensorflow-deeplearning/
├── README.md
├── requirements.txt
├── setup.py
├── main.py
├── config.yaml
├── env.example
├── Dockerfile
├── docker-compose.yml
├── notebooks/
│   ├── 01_neural_networks.ipynb
│   ├── 02_cnns.ipynb
│   ├── 03_rnns.ipynb
│   └── 04_custom_models.ipynb
├── src/
│   ├── __init__.py
│   ├── neural_networks.py
│   ├── cnns.py
│   ├── rnns.py
│   ├── transformers.py
│   ├── transfer_learning.py
│   ├── gans.py
│   ├── autoencoders.py
│   ├── custom_layers.py
│   ├── model_training.py
│   ├── model_deployment.py
│   ├── model_evaluation.py
│   ├── data_preprocessing.py
│   ├── visualization.py
│   └── utils/
│       ├── __init__.py
│       └── helpers.py
├── api/
│   ├── server.py
│   └── requirements.txt
├── examples/
│   ├── train_custom_model.py
│   └── transfer_learning_example.py
├── tests/
│   ├── test_neural_networks.py
│   └── test_cnns.py
├── models/
│   └── .gitkeep
└── data/
    └── .gitkeep

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

3. (Optional) For API server:

pip install -r api/requirements.txt

Usage

Running Python Scripts

# Using main entry point
python main.py --module neural_networks
python main.py --module cnns
python main.py --module rnns
python main.py --module transfer_learning
python main.py --module gans
python main.py --module autoencoders

# Direct module execution
python src/neural_networks.py
python src/cnns.py
python src/rnns.py

Running Example Scripts

python examples/train_custom_model.py
python examples/transfer_learning_example.py

Running Jupyter Notebooks

jupyter notebook notebooks/

Running Tests

python -m pytest tests/
# or
python -m unittest discover tests

Running API Server

# Using Python directly
python api/server.py

# Using Docker
docker-compose up tensorflow-api

# The API will be available at http://localhost:5000

Docker Deployment

# Build and run with Docker Compose
docker-compose up -d

# Run Jupyter notebook in Docker
docker-compose up jupyter

API Endpoints

• GET /health - Health check
• POST /predict - Single prediction
• POST /predict/batch - Batch predictions
• GET /model/info - Model information

Configuration

Edit config.yaml or create .env file from env.example to customize settings.

Contact

RSK World

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

License

This project is for educational purposes.