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Deep Learning With Python, 2nd Edition

Deep Learning With Python, 2nd Edition
Date: December 7th, 2021
ISBN: 1617296864
Language: English
Number of pages: 504 pages
Format: EPUB
Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.

In Deep Learning with Python, Second Edition you will learn:
• Deep learning from first principles
• Image classification & image segmentation
• Timeseries forecasting
• Text classification and machine translation
• Text generation, neural style transfer, and image generation

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks.

About the technology

Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science.

About the book
Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this 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 illustrations, and clear examples. You'll pick up the skills to start developing deep-learning applications.

What's inside
• Deep learning from first principles
• Image classification and image segmentation
• Time series forecasting
• Text classification and machine translation
• Text generation, neural style transfer, and image generation

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