Week 1: Introduction to Deep Learning
Course Generated Slides: Linear
Regression | Neural Networks
1. Introduction and Context
- Brief history of machine learning
- Types of machine learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Why deep learning?
- Key applications and successes
- Evolution of deep learning frameworks
2. Basic Neural Network Concepts
- From logistic regression to single neuron
- Basic architecture of neural networks
- Layers
- Weights and biases
- Forward propagation intuition
- Common activation functions
- Simple loss functions introduction
3. Deep Learning Frameworks
- PyTorch vs TensorFlow comparison
- Philosophy and approach differences
- Dynamic vs Static graphs
- Eager execution
- Development workflow differences
- Basic tensor operations overview
- Simple example concepts in both frameworks
- PyTorch's role in research
- TensorFlow's role in production
Required Reading
- Deep Learning Fundamentals
- Evolution of Neural Networks
- Deep Learning with PyTorch - Official PyTorch textbook covering deep
learning fundamentals and implementation
Video Resources
- Neural Networks -
3Blue1Brown's visual introduction to neural networks and deep learning concepts
Learning Objectives
- Understand the fundamental concepts of deep learning
- Differentiate between types of machine learning
- Grasp basic neural network architecture
- Understand the key differences between PyTorch and TensorFlow
Additional Resources