Introduction to Deep Learning
- Welcome
- What is a Neural Network?
- Supervised Learning with Neural Networks
- Why is Deep Learning taking off?
- About this Course
Basics of Neural Network Programming
- Binary Classification
- Logistic Regression
- Logistic Regression cost function
- Gradient Descent
- Derivatives
- Computation Graph
- Derivatives with a Computation Graph
- Logistic Regression Gradient descent
- Gradient descent on m examples
- Vectorization
- Vectorizing Logistic Regression
- Vectorizing Logistic Regression’s Gradient Computation
- Broadcasting in Python
- A note on python/numpy vectors
- Neural Networks Overview
- Neural Network Representation
- Computing a Neural Network’s Output
- Vectorizing across multiple examples
- Explanation for vectorized implementation
- Activation functions
- Why do you need non-linear activation functions?
- Derivatives of activation functions
- Gradient descent for neural networks
- Backpropagation intuition
- Random Initialization
Deep Neural Networks
- Deep L-layer Neural network
- Forward Propagation in a Deep Network
- Getting your matrix dimensions right
- Why deep representations?
- Building blocks of deep neural networks
- Forward and backward propagation
- Parameters vs Hyperparameters
- What does this have to do with the brain?
中文总结
