Deep Learning with Python presents the field of deep learning utilizing the Python language and the amazing Keras library. Composed by Keras maker and Google artificial intelligence scientist François Chollet, this book assembles your comprehension through natural clarifications and pragmatic models.
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About the Innovation
AI has gained amazing ground as of late. We went from close unusable discourse and picture acknowledgment, to approach human precision. We went from machines that couldn’t beat a genuine Go player, to crushing a best on the planet. Behind this advancement is deep learning—a blend of designing advances, best practices, and hypothesis that empowers an abundance of already outlandish savvy applications.
About the Book
Deep Learning with Python presents the field of deep learning utilizing the Python language and the ground-breaking Keras library. Composed by Keras maker and Google computer based intelligence specialist François Chollet, this book manufactures your comprehension through instinctive clarifications and down to earth models. You’ll investigate testing ideas and practice with applications in PC vision, common language handling, and generative models. When you finish, you’ll have the information and hands-on abilities to apply deep learning in your own activities.
- Deep learning from first standards
- Setting up your own deep-learning condition
- Picture order models
- Deep learning for content and groupings
- Neural style move, content age, and picture age
About the Peruser
Perusers need middle of the road Python abilities. No past involvement in Keras, TensorFlow, or AI is required.
About the Creator
François Chollet deals with deep learning at Google in Mountain View, CA. He is the maker of the Keras deep-learning library, just as a supporter of the TensorFlow AI system. He additionally does deep-learning research, with an attention on PC vision and the utilization of AI to formal thinking. His papers have been distributed at significant meetings in the field, remembering the Gathering for PC Vision and Example Acknowledgment (CVPR), the Meeting and Workshop on Neural Data Handling Frameworks (NIPS), the Universal Meeting on Learning Portrayals (ICLR), and others.
Chapter by chapter guide
Section 1 – Basics OF DEEP LEARNING
What is deep learning?
Before we start: the scientific structure squares of neural systems
Beginning with neural systems
Basics of AI
Section 2 – DEEP LEARNING By and by
Deep learning for PC vision
Deep learning for content and arrangements
Propelled deep-learning best practices
Generative deep learning
reference section A – Introducing Keras and its conditions on Ubuntu
informative supplement B – Running Jupyter scratch pad on an EC2 GPU occasion