Key FeaturesLearn how to implement advanced techniques in deep learning with Googles brainchild, TensorFlowExplore deep neural networks and layers of data abstraction with the help of this comprehensive guideReal-world contextualization through some deep learning problems concerning research and application Book DescriptionDeep 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 using TensorFlow 1.x.Throughout the book, youll 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, youll 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 learnLearn about machine learning landscapes along with the historical development and progress of deep learningLearn about deep machine intelligence and GPU computing with the latest TensorFlow 1.xAccess public datasets and utilize them using TensorFlow to load, process, and transform dataUse TensorFlow on real-world datasets, including images, text, and moreLearn how to evaluate the performance of your deep learning modelsUsing 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 applicationsAbout the AuthorGiancarlo 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.You can follow him at https://it.linkedin.com/in/giancarlozaccone.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.Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of Machine Learning and Natural Language Processing (NLP). He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as Machine Learning, Image Processing, Linear Algebra, Probability and Statistics, Data structures, Essential Mathematics for Computer Science. Next, he joined as a research scientist at the Industrial research and development lab at IST Networks, based in Egypt. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. Consequently, he was the main machine learning specialist in that company. Later on, he joined the Insight Centre for Data Analytics, the National University of Ireland at Galway as a Research Assistant working on building a Predictive Analytics Platform. Finally, he joined ADAPT Centre, Trinity College Dublin as a Research Engineer. His main role in ADAPT is to build prototypes and applications using ML and NLP techniques based on the research that is done within ADAPT.Table of ContentsGetting Started with Deep LearningFirst Look at TensorFlowUsing TensorFlow on a Feed-Forward Neural NetworkTensorFlow on a Convolutional Neural NetworkOptimizing TensorFlow AutoencodersRecurrent Neural NetworksGPU ComputingAdvanced TensorFlow ProgrammingAdvanced Multimedia Programming with TensorFlowReinforcement Learning