Practical Deep Learning with Python
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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: If you're 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 you're a deep-learning expert looking to get started with the Keras framework, you'll find this book to be the best Keras crash course available. If you're a graduate student studyin...
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: If you're 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 you're a deep-learning expert looking to get started with the Keras framework, you'll find this book to be the best Keras crash course available. If you're a graduate student studying deep learning in a formal setting, you'll 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. Even technically minded people who don't code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts. In order to use Keras, you'll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isn't required. You don't need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don't need an advanced mathematics background, either-high school-level mathematics should suffice in order to follow along.
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