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Deep Learning With Tensorflow Second Edition

Deep Learning with TensorFlow   Second Edition PDF

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Author: Giancarlo Zaccone
Publisher:
ISBN:
Size: 46.92 MB
Format: PDF, Mobi
Category : Data mining
Languages : en
Pages : 484
View: 2824


Book Description: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of 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 Gain real-world contextualization through some deep learning problems concerning research and application 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. What You Will Learn Apply deep machine intelligence and GPU computing with TensorFlow 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 In Detail 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, 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 approach This step-by-step guide explores common, and not so common, deep neural networks, and shows ho ...


Machine Learning With Tensorflow Second Edition

Machine Learning with TensorFlow  Second Edition PDF

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Author: Mattmann A. Chris
Publisher: Manning Publications
ISBN: 9781617297717
Size: 42.12 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 350
View: 788


Book Description: This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges. New and revised content expands coverage of core machine learning algorithms and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.


Deep Learning With Tensorflow 2 And Keras

Deep Learning with TensorFlow 2 and Keras PDF

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Author: Antonio Gulli
Publisher: Packt Publishing Ltd
ISBN: 1838827722
Size: 75.23 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 646
View: 4566


Book Description: Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks) to create new data that fits with existing patterns Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems.


Advanced Deep Learning With Tensorflow 2 And Keras

Advanced Deep Learning with TensorFlow 2 and Keras PDF

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Author: Rowel Atienza
Publisher: Packt Publishing Ltd
ISBN: 183882572X
Size: 34.40 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 512
View: 2744


Book Description: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.


Hands On Machine Learning With Scikit Learn Keras And Tensorflow 2nd Edition

Hands On Machine Learning with Scikit Learn  Keras  and TensorFlow  2nd Edition PDF

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Author: Aurélien Géron
Publisher:
ISBN:
Size: 15.40 MB
Format: PDF, ePub
Category :
Languages : en
Pages : 848
View: 5956


Book Description: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets.


Advanced Deep Learning With Tensorflow 2 And Keras Second Edition

Advanced Deep Learning with TensorFlow 2 and Keras   Second Edition PDF

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Author: Rowel Atienza
Publisher:
ISBN:
Size: 26.17 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages : 512
View: 1231


Book Description: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models - autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel...


Deep Learning With Tensorflow

Deep Learning with TensorFlow PDF

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Author: Giancarlo Zaccone
Publisher: Packt Publishing Ltd
ISBN: 1788831837
Size: 17.27 MB
Format: PDF
Category : Computers
Languages : en
Pages : 484
View: 6293


Book Description: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features 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 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, 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 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.


Deep Learning With Tensorflow 2 And Keras Second Edition

Deep Learning with TensorFlow 2 and Keras   Second Edition PDF

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Author: Antonio Gulli
Publisher:
ISBN:
Size: 51.87 MB
Format: PDF, ePub, Mobi
Category :
Languages : en
Pages : 646
View: 6904


Book Description: Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks) to create new data that fits with existing patterns Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems.


Python Deep Learning Second Edition

Python Deep Learning   Second Edition PDF

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Author: Peter Roelants
Publisher:
ISBN: 9781789348460
Size: 40.13 MB
Format: PDF, Docs
Category : Python (Computer program language)
Languages : en
Pages : 386
View: 2519


Book Description: Exploring an advanced state of the art deep learning models and its applications using Popular python libraries like Keras, Tensorflow, and Pytorch About This Book A strong foundation on 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. Who This Book Is For This book is for Data Science practitioners, Machine Learning Engineers and Deep learning aspirants who have a basic foundation of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired What You Will Learn Grasp mathematical theory behind neural networks and deep learning process. Investigate and resolve computer vision challenges using convolutional networks and capsule networks. Solve Generative tasks using Variational Autoencoders and Generative Adversarial Nets (GANs). Explore Reinforcement Learning and understand how agents behave in a complex environment. Implement complex natural language processing tasks using recurrent networks (LSTM, GRU), and attention models. In Detail With the surge of Artificial Intelligence in each and every application catering to both business and consumer needs, Deep Learning becomes the prime need of today and future market demands. This book explores deep learning and builds a strong deep learning mindset in order to put them into use in their smart artificial intelligence projects. This second edition builds strong grounds of deep learning, deep neural networks and how to train them with high-performance algorithms and popular python frameworks. You will uncover different neural networks architectures like convolutional networks, recurrent networks, long short term memory (LSTM) and solve problems across image recognition, natural language processing, and time-series prediction. You will also explore the newly evolved area of reinforcement learning and it will help you to understand the state-of-the-art algorithms which are the main engines behind popular game Go, Atari, and Dota. By the end of the book, you will be well versed with practical deep learning knowledge and its real-world applications Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.Pa ...


Praxiseinstieg Machine Learning Mit Scikit Learn Keras Und Tensorflow 2nd Edition

Praxiseinstieg Machine Learning Mit Scikit Learn  Keras und TensorFlow  2nd Edition PDF

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Author: Aurélien Géron
Publisher:
ISBN:
Size: 55.79 MB
Format: PDF, ePub
Category :
Languages : de
Pages : 852
View: 6812


Book Description: Eine Reihe technischer Durchbrüche beim Deep Learning haben das gesamte Gebiet des maschinellen Lernens in den letzten Jahren beflügelt. Inzwischen können sogar Programmierer, die kaum etwas über diese Technologie wissen, mit einfachen, effizienten Werkzeugen Machine-Learning-Programme implementieren. Dieses praxisorientierte Buch zeigt Ihnen wie. Mit konkreten Beispielen, einem Minimum an Theorie und zwei unmittelbar anwendbaren Python-Frameworks - Scikit-Learn und TensorFlow 2 - verhilft Ihnen der Autor Aurélien Géron zu einem intuitiven Verständnis der Konzepte und Tools für das Entwickeln intelligenter Systeme. Sie lernen eine Vielzahl von Techniken kennen, beginnend mit einfacher linearer Regression bis hin zu Deep Neural Networks. Die in jedem Kapitel enthaltenen Übungen helfen Ihnen, das Gelernte in die Praxis umzusetzen. Um direkt zu starten, benötigen Sie lediglich etwas Programmiererfahrung.