Python Deep Learning
Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
2. Auflage
Erschienen am 16. Januar 2019
Buch
Softcover
386 Seiten
978-1-78934-846-0 (ISBN)
Beschreibung
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries
Key Features
Build a strong foundation in neural networks and deep learning with Python libraries
Explore advanced deep learning techniques and their applications across computer vision and NLP
Learn how a computer can navigate in complex environments with reinforcement learning
Book 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 learn
Grasp the mathematical theory behind neural networks and deep learning processes
Investigate and resolve computer vision challenges using convolutional networks and capsule networks
Solve generative tasks using variational autoencoders and Generative Adversarial Networks
Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
Explore reinforcement learning and understand how agents behave in a complex environment
Get up to date with applications of deep learning in autonomous vehicles
Who 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.
Key Features
Build a strong foundation in neural networks and deep learning with Python libraries
Explore advanced deep learning techniques and their applications across computer vision and NLP
Learn how a computer can navigate in complex environments with reinforcement learning
Book 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 learn
Grasp the mathematical theory behind neural networks and deep learning processes
Investigate and resolve computer vision challenges using convolutional networks and capsule networks
Solve generative tasks using variational autoencoders and Generative Adversarial Networks
Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
Explore reinforcement learning and understand how agents behave in a complex environment
Get up to date with applications of deep learning in autonomous vehicles
Who 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.
Weitere Details
Auflage
2nd Revised edition
Sprache
Englisch
Verlagsort
Birmingham
Großbritannien
Zielgruppe
Für Beruf und Forschung
Editions-Typ
Überarbeitete Ausgabe
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 21 mm
Gewicht
719 gr
ISBN-13
978-1-78934-846-0 (9781789348460)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Weitere Ausgaben
Andere Ausgaben
Ivan Vasilev | Daniel Slater | Gianmario Spacagna
Python Deep Learning
Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
E-Book
06/2024
2. Auflage
Packt Publishing Limited
30,99 €
Als Download verfügbar
Personen
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, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python open source algorithmic trading library, which provides the infrastructure to experiment with different ML algorithms. The author holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski. 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 the 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.
Inhalt
Table of Contents
Machine Learning - An Introduction
Neural Networks
Deep Learning Fundamentals
Computer Vision With Convolutional Networks
Advanced Computer Vision
Generating images with GANs and Variational Autoencoders
Recurrent Neural Networks and Language Models
Reinforcement Learning Theory
Deep Reinforcement Learning for Games
Deep Learning in Autonomous Vehicles
Machine Learning - An Introduction
Neural Networks
Deep Learning Fundamentals
Computer Vision With Convolutional Networks
Advanced Computer Vision
Generating images with GANs and Variational Autoencoders
Recurrent Neural Networks and Language Models
Reinforcement Learning Theory
Deep Reinforcement Learning for Games
Deep Learning in Autonomous Vehicles