Module 1 – Neural Networks and Deep Learning

• Introduction to deep learning
• Neural Networks Basics
• Shallow neural networks
• Deep neural networks
• Live project : Starting with Deep learning

 

Module 2 – Improving DNN: Hyperparameter, Regularization and Optimization

• Regularization
• Reduces Overfitting
• Dropout
• Regularization
• Gradient descent
• Dropout
• How to pick Hyper parameter
• Softmax Regression
• Live Project : Training DNN

Module 3 – Convolutional Neural Networks (CNN)

• Foundations of CNN
• Padding and Pooling Layers
• Data Augmentation
• Live project : Face recognition
• Live Project : Object Detection

Module 4 – Sequence modeling

• Recurrent Neural Networks (RNN)
• Backpropagation
• Long Short term Memory (LSTM)
• Live Project : Language Model
• Live Project : Sequence Generation (Next word Prediction)

Module 5 – Natural Language Processing (NLP)

• Word Embedding
• Word2Vec and Sampling
• GloVe word vectors
• Live Project : Sentiment Classification