⚡ TensorFlow & PyTorch

Master the two leading deep learning frameworks for building and deploying AI models

← Back to CS Courses

TensorFlow & PyTorch Curriculum

12
Comprehensive Units
~90
Framework Concepts
25+
Hands-on Projects
40+
Code Examples
1

Framework Fundamentals

Introduction to TensorFlow and PyTorch ecosystems, installation, and basic concepts.

  • Framework comparison
  • Installation and setup
  • Computational graphs
  • Tensors and operations
  • Automatic differentiation
  • Development environments
  • Community and resources
  • Choosing the right framework
2

Tensor Operations

Master tensor creation, manipulation, and mathematical operations in both frameworks.

  • Tensor creation methods
  • Shape manipulation
  • Indexing and slicing
  • Mathematical operations
  • Broadcasting rules
  • GPU acceleration
  • Memory management
  • Performance optimization
3

Building Neural Networks

Learn to construct neural networks using high-level APIs and custom implementations.

  • Sequential models
  • Functional API
  • Custom layers
  • Model subclassing
  • Layer composition
  • Parameter initialization
  • Model inspection
  • Architecture patterns
4

Training and Optimization

Implement training loops, optimization algorithms, and loss functions.

  • Training loops
  • Loss functions
  • Optimizers
  • Learning rate scheduling
  • Gradient computation
  • Backpropagation
  • Gradient clipping
  • Training strategies
5

Data Loading and Preprocessing

Efficient data pipelines, preprocessing, and augmentation techniques.

  • Data loaders
  • Dataset APIs
  • Batch processing
  • Data augmentation
  • Preprocessing pipelines
  • Custom datasets
  • Memory-efficient loading
  • Parallel processing
6

Computer Vision Applications

Implement CNN architectures and computer vision tasks using both frameworks.

  • Convolutional layers
  • Popular CNN architectures
  • Image classification
  • Object detection
  • Transfer learning
  • Image preprocessing
  • Visualization techniques
  • Model interpretation
7

Natural Language Processing

Build NLP models including RNNs, transformers, and language models.

  • Text preprocessing
  • Embeddings
  • RNN implementations
  • Attention mechanisms
  • Transformer models
  • Pre-trained models
  • Fine-tuning strategies
  • Text generation
8

Advanced Training Techniques

Explore regularization, distributed training, and advanced optimization methods.

  • Regularization techniques
  • Batch normalization
  • Dropout variants
  • Mixed precision training
  • Distributed training
  • Multi-GPU setups
  • Gradient accumulation
  • Training diagnostics
9

Model Deployment

Deploy models to production environments using framework-specific tools.

  • Model serialization
  • TensorFlow Serving
  • TorchScript
  • ONNX format
  • Mobile deployment
  • Edge computing
  • API development
  • Performance monitoring
10

Custom Operations and Extensions

Create custom operations, layers, and extend framework functionality.

  • Custom operations
  • C++ extensions
  • CUDA kernels
  • Custom gradients
  • Function decorators
  • Plugin development
  • Performance profiling
  • Debugging techniques
11

Research and Experimentation

Use frameworks for research, prototyping, and implementing cutting-edge papers.

  • Research workflows
  • Experiment tracking
  • Hyperparameter tuning
  • Model versioning
  • Reproducibility
  • Paper implementations
  • Ablation studies
  • Benchmarking
12

Production and MLOps

Integrate frameworks into MLOps pipelines and production systems.

  • CI/CD for ML
  • Model monitoring
  • A/B testing
  • Version control
  • Container deployment
  • Kubernetes orchestration
  • Scaling strategies
  • Best practices

Unit 1: Framework Fundamentals

Introduction to TensorFlow and PyTorch ecosystems, installation, and basic concepts.

Framework Comparison

Understand the key differences, strengths, and use cases of TensorFlow and PyTorch.

TensorFlow PyTorch Comparison
TensorFlow offers production-ready tools and a mature ecosystem, while PyTorch provides dynamic computation graphs and intuitive research-oriented design. Both are excellent choices with different strengths.
# Framework Comparison
framework_comparison = {
  "tensorflow": {
    "strengths": [
      "Production deployment tools",
      "TensorBoard visualization",
      "TensorFlow Serving",
      "Mobile/edge deployment",
      "Mature ecosystem"
    ],
    "paradigm": "Static computation graphs (2.x uses eager by default)",
    "learning_curve": "Steeper initially, powerful once mastered",
    "best_for": ["Production systems", "Large-scale deployment", "Industry applications"]
  },
  "pytorch": {
    "strengths": [
      "Dynamic computation graphs",
      "Pythonic and intuitive",
      "Easy debugging",
      "Research flexibility",
      "Strong community"
    ],
    "paradigm": "Dynamic computation graphs (define-by-run)",
    "learning_curve": "More intuitive for Python developers",
    "best_for": ["Research", "Prototyping", "Educational purposes"]
  }
}

Installation and Setup

Learn proper installation procedures, environment setup, and configuration for both frameworks.

Installation Options:
• CPU-only versions for development and testing
• GPU-enabled versions for training acceleration
• Conda vs pip installation methods
• Docker containers for consistent environments
• Cloud platform integrations
GPU Setup Considerations:
Ensure CUDA and cuDNN versions are compatible with your framework version. Use conda for easier dependency management, especially with GPU libraries.
# Installation Commands
installation_guide = {
  "tensorflow": {
    "cpu_pip": "pip install tensorflow",
    "gpu_pip": "pip install tensorflow[and-cuda]",
    "conda": "conda install tensorflow-gpu",
    "verification": "import tensorflow as tf; print(tf.__version__)"
  },
  "pytorch": {
    "cpu_pip": "pip install torch torchvision torchaudio",
    "gpu_pip": "pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118",
    "conda": "conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia",
    "verification": "import torch; print(torch.__version__); print(torch.cuda.is_available())"
  },
  "gpu_requirements": ["NVIDIA GPU", "CUDA Toolkit", "cuDNN library", "Compatible drivers"]
}

Computational Graphs

Understand how computational graphs work in both static and dynamic paradigms.

Graph Types:
• Static graphs: Define then run (TensorFlow 1.x, TorchScript)
• Dynamic graphs: Define by run (PyTorch, TensorFlow 2.x eager)
• Hybrid approaches: Best of both worlds
Trade-offs:
Static graphs enable optimizations and deployment efficiency, while dynamic graphs provide flexibility and easier debugging. Modern frameworks offer both options.
# Computational Graph Concepts
graph_concepts = {
  "static_graphs": {
    "characteristics": ["Define once, run many times", "Can be optimized", "Better for deployment"],
    "tensorflow_example": "@tf.function decorator creates graphs from eager code",
    "pytorch_example": "torch.jit.script() or torch.jit.trace() for TorchScript"
  },
  "dynamic_graphs": {
    "characteristics": ["Define by run", "Flexible control flow", "Easy debugging"],
    "tensorflow_example": "Eager execution (default in TF 2.x)",
    "pytorch_example": "Default behavior in PyTorch"
  },
  "advantages": {
    "static": ["Optimization", "Deployment", "Performance"],
    "dynamic": ["Flexibility", "