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[체험판] Deep Learning with TensorFlow
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- 출간 정보
- 2017.04.22. 전자책, 종이책 동시 출간
- 파일 정보
- 7.6MB
- 44쪽
- ISBN
- 9781786460127
- ECN
- -
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<[체험판] Deep Learning with TensorFlow>
▶About This Book
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!
▶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.
▶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
▶In Detail
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 using 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.
▶Style and approach
This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
▶Editorial Review
This book introduces the core concepts of deep learning using the latest version of TensorFlow. This is Google’s open-source framework for mathematical, machine learningand deep learning capabilities released in 2011. After that, TensorFlow has achieved wide adoption from academia and research to industry and following that recently the most stable version 1.0 has been released with a unified API. TensorFlow provides the flexibility
needed to implement and research cutting-edge architectures while allowing users to focus on the structure of their models as opposed to mathematical details. Readers will learn deep learning programming techniques with the hands-on model building, data collection and transformation and even more!
Enjoy reading
▶About the Author
⦁ 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 is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a Researcher at the Insight Centre for Data Analytics, Ireland. Before that, he worked as a Lead Engineer at Samsung Electronics, Korea.
He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published several research papers concerning bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, DeepLearning4j, MXNet, and H2O.
⦁ 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 and Computer Vision. 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 CONTENTS
1: GETTING STARTED WITH DEEP LEARNING
2: FIRST LOOK AT TENSORFLOW
3: USING TENSORFLOW ON A FEED-FORWARD NEURAL NETWORK
4: TENSORFLOW ON A CONVOLUTIONAL NEURAL NETWORK
5: OPTIMIZING TENSORFLOW AUTOENCODERS
6: RECURRENT NEURAL NETWORKS
7: GPU COMPUTING
8: ADVANCED TENSORFLOW PROGRAMMING
9: ADVANCED MULTIMEDIA PROGRAMMING WITH TENSORFLOW
10: REINFORCEMENT LEARNING
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