Pattanayak Pro Deep Learning with TensorFlow

A Mathematical Approach to Advanced Artificial Intelligence in Python
1. Auflage 2017
ISBN: 978-1-4842-3096-1
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 412 Seiten

Reihe: Professional and Applied Computing

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
Pro Deep Learning with TensorFlow  provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures.
All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways.
You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. 
What You'll Learn

• Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning
• Deploy complex deep learning solutions in production using TensorFlow
• Carry out research on deep learning and perform experiments using TensorFlow

Who This Book Is For

Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Pattanayak, Santanu

Inhaltsverzeichnis

Chapter 1: Mathematical Foundations.- Chapter 2: Introduction to Deep Learning Concepts and TensorFlow.- Chapter 3: Convolutional Neural Networks.- Chapter 4: Natural Language Processing Using Recursive Neural Networks.- Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto Encoders.- Chapter 6: Advanced Neural Networks.

Über Autor(innen)

Santanu Pattanayak currently works at GE, Digital as a Senior Data Scientist. He has 10 years of overall work experience with six of years of experience in the data analytics/data science field and also has a background in development and database technologies. Prior to joining GE, Santanu worked in companies such as RBS, Capgemini, and IBM. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also devotes his time to data science hackathons and Kaggle competitions where he ranks within the top 500 across the globe. Santanu was born and brought up in West Bengal, India and currently resides in Bangalore, India with his wife.