Python Deep Learning

Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

AvIvan Vasilev,Daniel Slater

Häftad, Engelska, 2019

770 kr

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Beskrivning

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python librariesKey FeaturesBuild a strong foundation in neural networks and deep learning with Python librariesExplore advanced deep learning techniques and their applications across computer vision and NLPLearn how a computer can navigate in complex environments with reinforcement learningBook DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learnGrasp the mathematical theory behind neural networks and deep learning processesInvestigate and resolve computer vision challenges using convolutional networks and capsule networksSolve generative tasks using variational autoencoders and Generative Adversarial NetworksImplement complex NLP tasks using recurrent networks (LSTM and GRU) and attention modelsExplore reinforcement learning and understand how agents behave in a complex environmentGet up to date with applications of deep learning in autonomous vehiclesWho this book is forThis book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Produktinformation

Utgivningsdatum:2019-01-16
Mått:191 x 235 x 21 mm
Vikt:719 g
Format:Häftad
Språk:Engelska
Antal sidor:386
Upplaga:2
Förlag:Packt Publishing Limited
ISBN:9781789348460

Utforska kategorier

Webbprogrammering inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, with whom he continued its development. He has also worked as a machine learning engineer and researcher in medical image classification and segmentation with deep neural networks. Since 2017, he has focused on financial machine learning. He co-founded an algorithmic trading company, where he's the lead engineer.He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic. Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google. Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing. Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents. Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.

Innehållsförteckning

Table of ContentsMachine Learning – An IntroductionNeural NetworksDeep Learning FundamentalsComputer Vision With Convolutional NetworksAdvanced Computer VisionGenerating images with GANs and Variational AutoencodersRecurrent Neural Networks and Language ModelsReinforcement Learning TheoryDeep Reinforcement Learning for GamesDeep Learning in Autonomous Vehicles