Unlock the groundbreaking advances of deep learning with this extensively revised new 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.

order to begin deploying deep-learning technology to every problem that it could solve, we need to make it accessible to as many people as possible, including non- experts—people who aren’t researchers or graduate students.

This first chapter of Deep Learning for Natural Language Processing offers readers the basics of the Python language, NLP, and Deep Learning. First, we cover the beginner-level codes in the Pandas, NumPy, and SciPy

Feb 23, 2024 · Deep learning with Python : a hands-on introduction. Obscured text on back cover due to sticker attached.

This is the code repository for Python Deep Learning - Third Edition, published by Packt. Understand how deep neural networks work and apply them to real-world tasks

Part I serves as a brief introduction to machine learning, deep learning, and PyTorch. We explore the evolution of the field, from early rule-based systems to the present-day sophisticated algorithms, in an accelerated fashion. Part II explores the essential deep learning building blocks. Chapter 3 introduces a simple feed-forward neural network.

In developing the lessons in this book, I chose the best of breed Python deep learning library called Keras that abstracted away all of the complexity, ruthlessly leaving you an API containing only what you need to know to eficiently develop and evaluate neural network models.

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Dec 21, 2021 · 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...

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as …