TensorFlow Deep Learning Bootcamp

A comprehensive TensorFlow bootcamp project with updated setup instructions and migration guides for modern TensorFlow versions.

📋 Overview

This repository contains materials and setup instructions for a TensorFlow deep learning bootcamp. Originally designed for TensorFlow 1.3, it now includes comprehensive migration guides and setup instructions for TensorFlow 2.x.

🚀 Quick Start

Prerequisites

• Python 3.8-3.11 (TensorFlow 2.15+ supports Python 3.9-3.11)
• pip 19.0 or later
• 64-bit system
• At least 4GB RAM (8GB+ recommended)

Option 1: Automated Setup (Recommended)

Run the setup script to create a virtual environment with all dependencies:

chmod +x setup_venv.sh
./setup_venv.sh

To activate the environment:

source tfdeeplearning/bin/activate

Option 2: Manual Installation

Using Virtual Environment

# Create virtual environment
python -m venv tfdeeplearning

# Activate virtual environment
source tfdeeplearning/bin/activate  # Linux/Mac
# tfdeeplearning\Scripts\activate   # Windows

# Upgrade pip
pip install --upgrade pip

# Install TensorFlow and dependencies
pip install tensorflow numpy matplotlib jupyter pandas scikit-learn

Using Conda

# Create conda environment
conda create -n tfdeeplearning python=3.10

# Activate environment
conda activate tfdeeplearning

# Install packages
conda install tensorflow numpy matplotlib jupyter pandas scikit-learn

✅ Verify Installation

Run the installation test:

python src/installation-testing.py

Expected output should include:

• TensorFlow version information
• Basic tensor operations
• Device information (CPU/GPU)
• Simple neural network test
• Success message

📚 Documentation

Setup Guides

• TensorFlow Setup Guide - Comprehensive installation instructions, troubleshooting, and optimization tips
• Migration Guide - Complete guide for migrating from TensorFlow 1.x to 2.x

Additional Resources

• Installation Logs - Detailed conda environment creation logs
• Testing Guide - Installation verification procedures

🔄 Migration from TensorFlow 1.x

If you're working with older TensorFlow 1.x code, use our migration guide:

Key Changes in TensorFlow 2.x

• Eager execution by default (no more Sessions)
• Simplified APIs with better Keras integration
• tf.function for performance optimization
• Cleaner variable and model management

Quick Migration Example

# TensorFlow 1.x (deprecated)
import tensorflow as tf
sess = tf.Session()
result = sess.run(tensor)

# TensorFlow 2.x (current)
import tensorflow as tf
result = tensor.numpy()

For complete migration instructions, see docs/tensorflow_migration_guide.md.

🛠 Troubleshooting

Common Issues

GPU Warnings: CUDA/GPU warnings are normal for CPU-only installations and can be ignored.

Import Errors: Ensure your virtual environment is activated and TensorFlow is installed correctly.

Memory Issues: Reduce batch sizes or enable GPU memory growth:

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)

For detailed troubleshooting, see the setup guide.

🔧 Development Setup

Jupyter Notebook

# Install Jupyter (if not already installed)
pip install jupyter

# Start Jupyter
jupyter notebook

Environment Variables

# Disable oneDNN optimizations (if causing issues)
export TF_ENABLE_ONEDNN_OPTS=0

# Set logging level
export TF_CPP_MIN_LOG_LEVEL=2  # Suppress INFO/WARNING messages

📁 Project Structure

.
├── README.md                    # This file
├── docs/                        # Documentation
│   ├── tensorflow_setup_guide.md      # Detailed setup instructions
│   ├── tensorflow_migration_guide.md  # TF 1.x to 2.x migration
│   ├── installation.txt               # Installation logs
│   └── installation-testing.txt       # Testing procedures
├── src/                         # Source code
│   └── installation-testing.py        # Installation verification script
├── setup_venv.sh               # Automated environment setup
└── run-python-venv.sh          # Environment activation helper

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test with the verification script
5. Submit a pull request

📄 License

This project is part of a TensorFlow bootcamp educational material.

🆘 Support

If you encounter issues:

1. Check the troubleshooting section in the setup guide
2. Verify your installation with python src/installation-testing.py
3. Review the migration guide if working with older code
4. Open an issue with detailed error messages and system information