Book description
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deeplearning applications.Table of contents
 Deep Learning with Python
 Copyright
 dedication
 brief contents
 contents
 front matter

1 What is deep learning?

1.1 Artificial intelligence, machine learning, and deep learning
 1.1.1 Artificial intelligence
 1.1.2 Machine learning
 1.1.3 Learning rules and representations from data
 1.1.4 The “deep” in “deep learning”
 1.1.5 Understanding how deep learning works, in three figures
 1.1.6 What deep learning has achieved so far
 1.1.7 Don’t believe the shortterm hype
 1.1.8 The promise of AI
 1.2 Before deep learning: A brief history of machine learning
 1.3 Why deep learning? Why now?

1.1 Artificial intelligence, machine learning, and deep learning

2 The mathematical building blocks of neural networks
 2.1 A first look at a neural network

2.2 Data representations for neural networks
 2.2.1 Scalars (rank0 tensors)
 2.2.2 Vectors (rank1 tensors)
 2.2.3 Matrices (rank2 tensors)
 2.2.4 Rank3 and higherrank tensors
 2.2.5 Key attributes
 2.2.6 Manipulating tensors in NumPy
 2.2.7 The notion of data batches
 2.2.8 Realworld examples of data tensors
 2.2.9 Vector data
 2.2.10 Timeseries data or sequence data
 2.2.11 Image data
 2.2.12 Video data
 2.3 The gears of neural networks: Tensor operations
 2.4 The engine of neural networks: Gradientbased optimization
 2.5 Looking back at our first example
 Summary

3 Introduction to Keras and TensorFlow
 3.1 What’s TensorFlow?
 3.2 What’s Keras?
 3.3 Keras and TensorFlow: A brief history
 3.4 Setting up a deep learning workspace
 3.5 First steps with TensorFlow

3.6 Anatomy of a neural network: Understanding core Keras APIs
 3.6.1 Layers: The building blocks of deep learning
 3.6.2 From layers to models
 3.6.3 The “compile” step: Configuring the learning process
 3.6.4 Picking a loss function
 3.6.5 Understanding the fit() method
 3.6.6 Monitoring loss and metrics on validation data
 3.6.7 Inference: Using a model after training
 Summary

4 Getting started with neural networks: Classification and regression
 4.1 Classifying movie reviews: A binary classification example

4.2 Classifying newswires: A multiclass classification example
 4.2.1 The Reuters dataset
 4.2.2 Preparing the data
 4.2.3 Building your model
 4.2.4 Validating your approach
 4.2.5 Generating predictions on new data
 4.2.6 A different way to handle the labels and the loss
 4.2.7 The importance of having sufficiently large intermediate layers
 4.2.8 Further experiments
 4.2.9 Wrapping up
 4.3 Predicting house prices: A regression example
 Summary
 5 Fundamentals of machine learning
 6 The universal workflow of machine learning
 7 Working with Keras: A deep dive
 8 Introduction to deep learning for computer vision
 9 Advanced deep learning for computer vision
 10 Deep learning for timeseries
 11 Deep learning for text
 12 Generative deep learning
 13 Best practices for the real world
 14 Conclusions
 index
Product information
 Title: Deep Learning with Python, Second Edition
 Author(s):
 Release date: November 2021
 Publisher(s): Manning Publications
 ISBN: 9781617296864
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