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Who should read this book If youre a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning If youre a deep-learning expert looking to get started with the Keras framework, youll find this book to be the best Keras crash course available If youre a graduate student studying deep learning in a formal setting, youll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices About This Book This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether youre a practicing machine-learning engineer, a software developer, or a college student, youll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. Youll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems. The code examples use the Python deep-learning framework Keras, with Tensor- Flow as a back-end engine. Keras, one of the most popular and fastest-growing deeplearning frameworks, is widely recommended as the best tool to get started with deep learning. After reading this book, youll have a solid understand of what deep learning is, when its applicable, and what its limitations are. Youll be familiar with the standard workflow for approaching and solving machine-learning problems, and youll know how to address commonly encountered issues. Youll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation, and more. This book is written for people with Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers. Even technically minded people who dont code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts. In order to use Keras, youll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isnt required. You dont need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You dont need an advanced mathematics background, eitherhigh schoollevel mathematics should suffice in order to follow along. Read more Deep Learning with Python Deep Learning with R Deep Learning with Francois Chollet

Details e-book Deep Learning with Python

🗸 Author(s): Francois Chollet
🗸 Title: Deep Learning with Python
🗸 Rating : 4.5 from 5 stars (1317 reviews)
🗸 Languange: English
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, PC and Amazon Kindle

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