Python Deep Learning: Develop Your First Neural Network In Python Using TensorFlow, Keras, And PyTorch - Step-by-Step Tutorial For Beginners
Language: English
Published by Independently published, 2019
- Softcover
- Used
Condition: Used - As new
US$ 27.04
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Build your own Neural Network today with easy-to-follow instructions and examples that cover the fundamentals of deep learning. You'll learn to create your own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano, all for a fraction of the cost of traditional textbooks. The book aims to deepen your understanding of deep learning, guide you in setting up your coding environment, and help you progress from a beginner to a professional. It is designed for complete novices, those looking to enhance their Python skills for deep learning, educators seeking effective teaching methods, and students focused on programming, neural networks, machine learning, and deep learning. To get started, you need Python 3.X, TensorFlow, Keras, and PyTorch installed on your computer. The author provides guidance on installing the necessary Python libraries. Inside, you'll find an overview of deep learning, artificial neural networks, library exploration, installation and setup, and basics of TensorFlow, Keras, and PyTorch. The book includes chapters on creating Convolutional and Recurrent Neural Networks. Written in accessible language, it minimizes complex math to cater to beginners. Each chapter features unique Neural Network architectures, along with Python code snippets and explanations to clarify the code's functionality, supplemented by screenshots of expected outputs. After reading, you'll be equipped to build your own.…
Seller Inventory # d128c408-102d-4f3f-8182-04e8dfd059a9
- Title
- Python Deep Learning: Develop Your First Neural Network In Python Using TensorFlow, Keras, And PyTorch - Step-by-Step Tutorial For Beginners
- Author
- Samuel Burns
- Publisher
- Independently published
- Publication year
- 2019
- Condition
- As New
- Binding
- Softcover
- Language
- English
- ISBN 10
- 1092562222
- ISBN 13
- 9781092562225
Get Your Copy Now!!
Why this book?
Book Objectives
The following are the objectives of this book:
- To help you understand deep learning in detail
- To help you know how to get started with deep learning in Python by setting up the coding environment.
- To help you transition from a deep learning Beginner to a Professional.
- To help you learn how to develop a complete and functional artificial neural network model in Python on your own.
Who this Book is for?
The author targets the following groups of people:
- Anybody who is a complete beginner to deep learning with Python.
- Anybody in need of advancing their Python for deep learning skills.
- Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning.
What do you need for this Book?
You are required to have installed the following on your computer:
- Python 3.X.
- TensorFlow .
- Keras .
- PyTorch
The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest.
What is inside the book?
- What is Deep Learning?
- An Overview of Artificial Neural Networks.
- Exploring the Libraries.
- Installation and Setup.
- TensorFlow Basics.
- Deep Learning with TensorFlow.
- Keras Basics.
- PyTorch Basics.
- Creating Convolutional Neural Networks with PyTorch.
- Creating Recurrent Neural Networks with PyTorch.
From the back cover.
Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.
"Synopsis" may belong to another edition of this title.
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