Deep Learning for Self-Driving Cars - Introduction Lecture Notes

Jul 7, 2024

Deep Learning for Self-Driving Cars - Introduction Lecture Notes

Course Overview

  • Course Code: 6.S094
  • Focus: Deep Learning for Self-Driving Cars
  • Part of a series on deep learning in January 2019
  • Content available at deeplearning.mit.edu
  • Materials include videos, lecture slides, code, GitHub resources
  • Assignments emailed to registered students
  • Contact: hcai@mit.edu (Human Centered AI)

Introduction to Deep Learning

  • Definition: Extracts useful patterns from data with minimal human intervention
  • Core Aspect: Optimization of neural networks
  • Libraries: Python, TensorFlow, PyTorch
  • Challenges: Asking good questions, obtaining, and organizing good data

Reasons for Recent Breakthroughs

  • Data Availability: Digitization and easy distributed access
  • Hardware Advances: CPUs, GPUs, ASICs, TPUs
  • Community: Collaborative global community (GitHub, etc.)
  • Tooling: Higher levels of abstraction (TensorFlow, PyTorch)
  • Applications: Face recognition, scene understanding, NLP, medical diagnosis, autonomous driving, digital assistants, ads, recommender systems, deep reinforcement learning

Philosophical Context

  • Historical Dream: AI inspired by mythology and cultural visions (Frankenstein, Ex Machina)
  • Deep Learning: Core of the effort to mimic human intelligence

History of neural networks

  • 1940s: Neural networks conceptual origin
  • Decades of Development: Perceptron (1950s), backpropagation, restricted Boltzmann machines, recurrent neural networks (1970s-80s), convolutional neural networks, MNIST dataset, LSTM
  • 2006: Rebranding as Deep Learning and the birth of ImageNet
  • Recent Milestones: GANs (2014), DeepFace, AlphaGo, capsule networks, NLP breakthroughs (BERT)

Practical Example: Training a MNIST Model

  1. Import TensorFlow Library
  2. Load MNIST Dataset
  3. Build Neural Network Layers
  4. Train the Model
  5. Evaluate the Model
  6. Deploy for Predictions

Tooling and Libraries

  • TensorFlow: Popular deep learning library from Google
  • Ecosystem: Includes Keras, TensorFlow.js, TensorFlow Lite, Google Colab, TPU, TensorBoard, TensorFlow Hub
  • Documentation and Tutorials: Thorough documentation and educational materials available

Core Concepts in Deep Learning & AI

  • Forming Representations: Higher-level abstractions of data
  • Compression in Science: Simpler representations make complex problems manageable
  • Representations: Topology mapping simplifies complex data for easier analysis and manipulation
  • Special Aspect of Deep Learning: Reduces the need for human expert intervention

Challenges and Ethical Considerations

  • Optimizing Objective Functions: Risks unintended consequences (e.g., reinforcement learning agent exploits rewards in unintended ways)
  • AI Safety: Human-in-the-loop necessary for ethical and safe AI deployment

Current Gaps and Limitations

  • Real-world Applications: Most robotics and autonomous systems still rely on non-ml methods
  • Perception and Understanding: Image classification ≠ scene understanding
  • General Challenges: Variation in datasets, lighting, human-level perception system complexity
  • Overfitting and Regularization: Challenges with generalization from training to real-world data

Neural Networks Basics

  • Inspired by Biological Neurons: Simplified computational units
  • Structure: Inputs, weights, biases, activation functions, outputs
  • Comparison with Human Brain: Differences in efficiency, learning algorithms, structure

Key Neural Network Concepts

  • Activation Functions: ReLUs, Sigmoids, etc.
  • Loss Functions: Mean Squared Error (regression), Cross-Entropy (classification)
  • Backpropagation: Algorithm for adjusting weights
  • Learning Rate: Speed at which the network learns
  • Stochastic Gradient Descent: Optimization algorithm
  • Regularization Techniques: Dropout, normalization (batch, layer, etc.), early stopping

Convolutional Neural Networks (CNN) & Visual Data

  • Image Classification: Using spatial invariance in images
  • Progression: AlexNet, GoogLeNet, ResNet, SENet
  • Object Detection and Localization: Region-based (R-CNN), Single-shot (YOLO, SSD)
  • Semantic Segmentation: Pixel-level classification
  • Transfer Learning: Using pre-trained networks for specialized tasks
  • Autoencoders: Compressing and reconstructing data for efficient representation

Advanced Techniques

  • Generative Adversarial Networks (GANs): Generator and discriminator for creating realistic images
  • Natural Language Processing (NLP): Word embeddings (Word2Vec), recurrent neural networks (RNN), encoder-decoder architectures, attention mechanisms

Automation and Future Directions

  • AutoML and Neural Architecture Search: Automating neural network design
  • Deep Reinforcement Learning: Agents learning via sparse rewards and self-play (e.g., AlphaGo, robotics)
  • Focus: Minimizing human intervention through advanced learning techniques

Conclusion

  • Notes on AI applications, progress, current limits, and future directions
  • Video, code, and additional resources on deeplearning.mit.edu

Thank you!