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Deep Learning with Python Full Course Outline |

**What is deep learning?**

1.1 Artificial intelligence, machine learning, and 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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