Deep Learning with TensorFlow - Second Edition

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide

Key Features

• Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
• Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
• Real-world contextualization through some deep learning problems concerning research and application

Book Description

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data and has been fully updated to the latest version of TensorFlow 1.x.

Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.

After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

What you will learn

• Learn about machine learning landscapes along with the historical development and progress of deep learning
• Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
• Access public datasets and utilize them using TensorFlow to load, process, and transform data
• Use TensorFlow on real-world datasets, including images, text, and more
• Learn how to evaluate the performance of your deep learning models
• Using deep learning for scalable object detection and mobile computing
• Train machines quickly to learn from data by exploring reinforcement learning techniques
• Explore active areas of deep learning research and applications

Who This Book Is For

The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

Giancarlo Zaccone has more than ten years of experience in managing research projects both in scientific and industrial areas. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects relating to parallel computing and scientific visualization. Currently, he is a system and software engineer at a consulting company developing and maintaining software systems for space and defense applications. He is author of the following Packt volumes: Python Parallel Programming Cookbook and Getting Started with TensorFlow.

Md. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures, focusing C/C++, Java, Scala, R, and Python and big data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Hadoop, and MapReduce. His research interests include machine learning, deep learning, Semantic Web, big data, and bioinformatics. He is the author of the book titled Large-Scale Machine Learning with Spark, Packt Publishing. He is a Software Engineer and Researcher currently working at the Insight Center for Data Analytics, Ireland. He is also a Ph.D. candidate at the National University of Ireland, Galway. He also holds a BS and an MS degree in Computer Engineering. Before joining the Insight Centre for Data Analytics, he had been working as a Lead Software Engineer with Samsung Electronics, where he worked with the distributed Samsung R&D centers across the world, including Korea, India, Vietnam, Turkey, and Bangladesh. Before that, he worked as a Research Assistant in the Database Lab at Kyung Hee University, Korea. He also worked as an R&D Engineer with BMTech21 Worldwide, Korea. Even before that, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh.