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| Deep Learning with Python Full Course Outline |
What is deep learning?
1.1 AI(artificial Intelligence), ML(Machine Learning), and DL(Deep Learning)
1.2 Before deep learning: a brief history of machine learning
1.3 Why deep learning? Why now?
Before we begin: the mathematical building blocks of neural networks
2.1 A first look at a neural network
2.2 Data representations for neural networks
2.3 The gears of neural networks: tensor operations
2.4 The engine of neural networks: gradient-based optimization
2.5 Looking back at our first example
Getting started with neural networks
3.1 Anatomy of a neural network
3.2 Introduction to Keras 61 Keras, TensorFlow, Theano, and CNTK
3.3 Setting up a deep-learning workstation
3.4 Classifying movie reviews: a binary classification example
3.5 Classifying newswires: a multiclass classification
3.6 Predicting house prices: a regression example
4 Fundamentals of machine learning
4.1 Four branches of machine learning
4.2 Evaluating machine-learning models
4.3 Data preprocessing, feature engineering, and feature learning
4.4 Overfitting and under fitting 104 Reducing the network’s size
4.5 The universal workflow of machine learning
PART(2)
DEEP LEARNING IN PRACTICE
Deep learning for computer vision
5.1 Introduction to convnets
5.2 Training a convnet from scratch on a small
5.3 Using a p retrained convnet
5.4 Visualizing what convents learn
Deep learning for text and sequences
6.1 Working with text data
6.2 Understanding recurrent neural networks
6.3 Advanced use of recurrent neural networks
6.4 Sequence processing with convnets
Advanced deep-learning best practices
7.1 Going beyond the Sequential model: the Keras functional API
7.2 Inspecting and monitoring deep-learning models using Keras callbacks and Tensor Board
7.3 Getting the most out of your models
Generative deep learning
8.1 Text generation with LSTM
8.2 Deep Dream Implementing Deep Dream in Keras
8.3 Neural style transfer
8.4 Generating images with variational autoencoders
8.5 Introduction to generative adversarial networks
Conclusions
9.1 Key concepts in review
9.2 The limitations of deep learning
9.3 The future of deep learning
9.4 Staying up to date in a fast-moving field
9.5 Final words
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