Neural Networks Basics

• Binary Classification
• Logistic Regression
• Logistic Regression Cost Function
• Gradient Descent
• Derivatives
• Computation graph
• Derivatives with a Computation Graph
• Logistic Regression Gradient Descent

Vectorization

• Vectorizing Logistic Regression
• Vectorizing Logistic Regression’s Gradient Output
• Broadcasting in Python
• A note on python/numpy vectors
• Quick tour of Jupyter/iPython Notebooks
• Explanation of logistic regression cost function (optional)

Shallow neural networks

• Neural Networks Overview
• Deep Learning Honor Code
• Logistic Regression with a Neural Network mindset
• 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 (optional)
• 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?
• Deep Neural Network – Application

Key concepts on Deep Neural Networks

• Building your Deep Neural Network: Step by Step
• Deep Neural Network – Application

Foundations of Convolutional Neural Networks

• Computer Vision
• Edge Detection
• Padding
• Strided Convolutions
• Convolutions Over Volume
• One Layer of a Convolutional Network
• Simple Convolutional Network Example
• Pooling Layers
• Why Convolutions?

Deep convolutional models: case studies

Object detection

• Object Localization
• Landmark Detection
• Object Detection
• Convolutional Implementation of Sliding Windows
• Bounding Box Predictions
• Intersection Over Union
• Non-max Suppression
• Anchor Boxes
• YOLO Algorithm

• Special applications: Face recognition & Neural style transfer

• What is face recognition?
• Siamese Network
• Triplet Loss
• Face Verification and Binary Classification
• What is neural style transfer?
• What are deep ConvNets learning?
• Cost Function
• Content Cost Function
• Style Cost Function
• 1D and 3D Generalizations
• Face Recognition for the Happy House

Recurrent Neural Networks

• Why sequence models
• Notation
• Recurrent Neural Network Model
• Back propagation through time
• Different types of RNNs
• Language model and sequence generation
• Sampling novel sequences
• Vanishing gradients with RNNs
• Gated Recurrent Unit (GRU)
• Long Short Term Memory (LSTM)
• Bidirectional RNN
• Deep RNNs
• Building a recurrent neural network – step by step
• Dinosaur Island – Character-Level Language Modeling
• Other-Jazz improvisation with LSTM

Natural Language Processing & Word Embeddings

• Word Representation
• Using word embeddings
• Properties of word embeddings
• Embedding matrix
• Learning word embeddings
• Word2Vec
• Negative Sampling
• GloVe word vectors
• Sentiment Classification
• Debiasing word embeddings
• Operations on word vectors – Debiasing

Sequence models & Attention mechanism

• Basic Models
• Picking the most likely sentence
• Beam Search
• Refinements to Beam Search
• Error analysis in beam search
• Bleu Score (optional)
• Attention Model Intuition
• Attention Model
• Speech recognition
• Trigger Word Detection
• Neural Machine Translation with Attention
• Trigger word detection