Image: Convolutional Neural Networks

• What is computer vision?
• Why Convolutions (CNN)?
• Introduction to CNN
• Train a simple convolutional neural net
• Explore the design space for convolutional nets
• Pooling layer motivation in CNN
• Design a convolutional layer application
• Understanding and visualizing a CNN
• Transfer learning and fine-tuning CNN

Detection & Recognition

• Padding & Edge Detection
• Strided Convolutions
• Networks in Networks and 1x1 Convolutions
• Inception Network Motivation
• Data Augmentation
• Object Localization
• Landmark Detection
• Object Detection
• Bounding Box Predictions
• YOLO Algorithm
• What is face recognition?
• One Shot Learning
• Siamese Network
• Triplet Loss
• Face Verification and Binary Classification
• What is neural style transfer?
• What are deep ConvNets learning?

Recurrent Neural Networks

• Why use sequence models?
• Recurrent Neural Network Model
• Notation
• Back-propagation through time (BTT)
• 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

Models of Recurrent Neural Networks

• Sequence models & Attention mechanism
• Basic Models
• Picking the most likely sentence
• Beam Search
• Refinements to Beam Search
• Error analysis in beam search
• Attention Model Intuition
• Attention Model
• Speech recognition
• Trigger Word Detection