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Deep Learning with Python

Paperback Engels 2017
Verkooppositie 5125
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Samenvatting

Deep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.

'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.

KEY FEATURES
- 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

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.

Specificaties

ISBN13:9781617294433
Taal:Engels
Bindwijze:paperback
Aantal pagina's:384
Druk:1
Verschijningsdatum:28-12-2017

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Over Francois Chollet

Francois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io.

Andere boeken door Francois Chollet

Inhoudsopgave

PART 1: THE FUNDAMENTALS OF DEEP LEARNING
1. WHAT IS DEEP LEARNING?
2. BEFORE WE START: THE MATHEMATICAL BLOCKS OF NEURAL NETWORKS
3. GETTING STARTED WITH NEURAL NETWORKS
4. FUNDAMENTALS OF MACHINE LEARNING

PART 2: DEEP LEARNING IN PRACTICE
5. DEEP LEARNING FOR COMPUTER VISION
6. DEEP LEARNING FOR TEXT AND SEQUENCES
7. ADVANCED DEEP LEARNING BEST PRACTICES
8. GENERATIVE DEEP LEARNING
9. CONCLUSIONS

APPENDIXES
APPENDIX A: INSTALLING KERAS AND ITS DEPENDENCIES ON UBUNTU
APPENDIX B: RUNNING JUPYTER NOTEBOOKS ON AN EC2 GPU INSTANCE

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