Book description
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
About the Technology
Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.
About the Book
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
What's Inside
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image-classification models
- Deep learning for text and sequences
- Neural style transfer, text generation, and image generation
About the Reader
Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.
About the Author
François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
Quotes
The clearest explanation of deep learning I have come across...it was a joy to read.
- Richard Tobias, Cephasonics
An excellent hands-on introductory title, with great depth and breadth.
- David Blumenthal-Barby, Babbel
Bridges the gap between the hype and a functioning deep-learning system.
- Peter Rabinovitch, Akamai
The best resource for becoming a master of Keras and deep learning.
- Claudio Rodriguez, Cox Media Group
Publisher resources
Table of contents
- Deep Learning with Python
- Copyright
- Brief Table of Contents
- Table of Contents
- Preface
- Acknowledgments
- About this Book
- About the Author
- About the Cover
- Part 1. Fundamentals of deep learning
-
Chapter 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 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 short-term 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
-
Chapter 2. Before we begin: the mathematical building blocks of neural networks
- 2.1. A first look at a neural network
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2.2. Data representations for neural networks
- 2.2.1. Scalars (0D tensors)
- 2.2.2. Vectors (1D tensors)
- 2.2.3. Matrices (2D tensors)
- 2.2.4. 3D tensors and higher-dimensional tensors
- 2.2.5. Key attributes
- 2.2.6. Manipulating tensors in Numpy
- 2.2.7. The notion of data batches
- 2.2.8. Real-world 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: gradient-based optimization
- 2.5. Looking back at our first example
-
Chapter 3. Getting started with neural networks
- 3.1. Anatomy of a neural network
- 3.2. Introduction to Keras
- 3.3. Setting up a deep-learning workstation
- 3.4. Classifying movie reviews: a binary classification example
-
3.5. Classifying newswires: a multiclass classification example
- 3.5.1. The Reuters dataset
- 3.5.2. Preparing the data
- 3.5.3. Building your network
- 3.5.4. Validating your approach
- 3.5.5. Generating predictions on new data
- 3.5.6. A different way to handle the labels and the loss
- 3.5.7. The importance of having sufficiently large intermediate layers
- 3.5.8. Further experiments
- 3.5.9. Wrapping up
- 3.6. Predicting house prices: a regression example
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Chapter 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 underfitting
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4.5. The universal workflow of machine learning
- 4.5.1. Defining the problem and assembling a dataset
- 4.5.2. Choosing a measure of success
- 4.5.3. Deciding on an evaluation protocol
- 4.5.4. Preparing your data
- 4.5.5. Developing a model that does better than a baseline
- 4.5.6. Scaling up: developing a model that overfits
- 4.5.7. Regularizing your model and tuning your hyperparameters
- Part 2. Deep learning in practice
- Chapter 5. Deep learning for computer vision
-
Chapter 6. Deep learning for text and sequences
- 6.1. Working with text data
- 6.2. Understanding recurrent neural networks
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6.3. Advanced use of recurrent neural networks
- 6.3.1. A temperature-forecasting problem
- 6.3.2. Preparing the data
- 6.3.3. A common-sense, non-machine-learning baseline
- 6.3.4. A basic machine-learning approach
- 6.3.5. A first recurrent baseline
- 6.3.6. Using recurrent dropout to fight overfitting
- 6.3.7. Stacking recurrent layers
- 6.3.8. Using bidirectional RNNs
- 6.3.9. Going even further
- 6.3.10. Wrapping up
- 6.4. Sequence processing with convnets
- Chapter 7. Advanced deep-learning best practices
- Chapter 8. Generative deep learning
- Chapter 9. Conclusions
- Appendix A. Installing Keras and its dependencies on Ubuntu
- Appendix B. Running Jupyter notebooks on an EC2 GPU instance
- Index
- List of Figures
- List of Tables
- List of Listings
Product information
- Title: Deep Learning with Python
- Author(s):
- Release date: December 2017
- Publisher(s): Manning Publications
- ISBN: 9781617294433
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