Deep Learning with TensorFlow

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.About This BookLearn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlowExplore deep neural networks and layers of data abstraction with the help of this comprehensive guideGain real-world contextualization through some deep learning problems concerning research and applicationWho This Book Is ForThe book is for 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.What You Will LearnApply deep machine intelligence and GPU computing with TensorFlowAccess public datasets and use TensorFlow to load, process, and transform the dataDiscover how to use the high-level TensorFlow API to build more powerful applicationsUse deep learning for scalable object detection and mobile computingTrain machines quickly to learn from data by exploring reinforcement learning techniquesExplore active areas of deep learning research and applicationsIn DetailDeep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.Style and approachThis step-by-step guide explores common, and not so common, deep neural networks, and shows how they can be exploited in the real world with complex raw data. Benefit from practical examples, and learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow v1.7.

Key Features

• Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow v1.7
• Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
• Gain real-world contextualization through some deep learning problems concerning research and application

Book Description

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.

This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow v1.7, combined with other open source Python libraries.

Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.

You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

What you will learn

• Apply deep machine intelligence and GPU computing with TensorFlow v1.7
• Access public datasets and use TensorFlow to load, process, and transform the data
• Discover how to use the high-level TensorFlow API to build more powerful applications
• Use 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 for 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 over ten years of experience in managing research projects in scientific and industrial areas. Giancarlo worked as a researcher at the CNR, the National Research Council of Italy. As part of his data science and software engineering projects, he gained experience in numerical computing, parallel computing, and scientific visualization. Currently, Giancarlo is a senior software and system engineer, based in the Netherlands. Here he tests and develops software systems for space and defense applications. Giancarlo holds a master's degree in Physics from the Federico II of Naples and a 2nd level postgraduate master course in Scienti fi c Computing from La Sapienza of Rome. Giancarlo is the author of the following books: Python Parallel Programminng Cookbook, Getting Started with TensorFlow, Deep Learning with TensorFlow, all by Packt Publishing. Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also pursuing his PhD at the RWTH Aachen University, Aachen, Germany. He holds BSc and MSc degrees in Computer Science. Before joining Fraunhofer FIT, Rezaul had been working as a researcher at Insight Centre for Data Analytics, Ireland. Previously, he worked as a Lead Engineer at Samsung Electronics. He also worked as a research assistant at Database Lab, Kyung Hee University, Korea and as an R&D engineer with BMTech21 Worldwide, Korea. Rezaul has over 9 years of experience in research and development with a solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several research papers and technical articles concerning Bioinformatics, Semantic Web, Big Data, Machine Learning and Deep Learning using Spark, Kafka, Docker, Zeppelin, Hadoop, and MapReduce. Rezaul is also equally competent with (deep) machine learning libraries such as Spark ML, Keras, Scikit-learn, TensorFlow, DeepLearning4j, MXNet, and H2O. Moreover, Rezaul is the author of the following books: Large-Scale Machine Learning with Spark, Deep Learning with TensorFlow, Scala and Spark for Big Data Analytics, Predictive Analytics with TensorFlow, Scala Machine Learning Projects, all by Packt Publishing.