Antonio Gulli's Books

TensorFlow 1.x Deep Learning Cookbook

Over 90 unique recipes to solve artificial-intelligence driven problems with Python

By: Antonio Gulli,Amita Kapoor

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TensorFlow 1.x Deep Learning Cookbook

Over 90 unique recipes to solve artificial-intelligence driven problems with Python

By: Antonio Gulli,Amita Kapoor

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.

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526

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Packt Publishing Ltd

Published Date

ISBN 10

1788291867

ISBN 13

9781788291866

Google Anthos in Action

Manage hybrid and multi-cloud Kubernetes clusters

By: Antonio Gulli

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Google Anthos in Action

Manage hybrid and multi-cloud Kubernetes clusters

By: Antonio Gulli

Learn multicloud deployment on Anthos directly from the Google development team! Anthos delivers a consistent management platform for deploying and operating Linux and Windows applications anywhere—multi-cloud, edge, on-prem, bare metal, or VMware. Summary In Google Anthos in Action you will learn: How Anthos reduces your dependencies and stack-bloat Running applications across multiple clouds and platforms Handling different workloads and data Adding automation to speed up code delivery Modernizing infrastructure with microservices and Service Mesh Policy management for enterprises Security and observability at scale Google Anthos in Action demystifies Anthos with practical examples of Anthos at work and invaluable insights from the Google team that built it. You’ll learn how to use this modern, Kubernetes-based cloud platform to balance costs, automate security, and run your software literally anywhere. The book is full of Google-tested patterns that will boost efficiency across the development lifecycle. It’s an absolutely essential guide for anyone working with Anthos, or delivering software in a cloud-centric world. About the technology The operations nightmare: modern applications run on-prem, in the cloud, at the edge, on bare metal, in containers, over VMs, in any combination. And you’re expected to handle the rollouts, dataOps, security, performance, scaling, backup, and whatever else comes your way. Google Anthos feels your pain. This Kubernetes-based system simplifies hybrid and multicloud operations, providing a single platform for deploying and managing your applications, wherever they live. About the book Google Anthos in Action introduces Anthos and shows you how it can simplify operations for hybrid cloud systems. Written by 17 Googlers, it lays out everything you can do with Anthos, from Kubernetes deployments to AI models and edge computing. Each fully illustrated chapter opens up a different Anthos feature, with exercises and examples so you can see Anthos in action. You’ll appreciate the valuable mix of perspectives and insight this awesome team of authors delivers. What's inside Reduce dependencies and stack-bloat Run applications across multiple clouds and platforms Speed up code delivery with automation Policy management for enterprises Security and observability at scale About the reader For software and cloud engineers with experience using Kubernetes. About the author Google Anthos in Action is written by a team of 17 Googlers involved with Anthos development, and Google Cloud Certified Fellows assisting customers in the field. Table of Contents 1 Overview of Anthos 2 One single pane of glass 3 Computing environment built on Kubernetes 4 Anthos Service Mesh: Security and observability at scale 5 Operations management 6 Bringing it all together 7 Hybrid applications 8 Working at the edge and the telco world 9 Serverless compute engine (Knative) 10 Networking environment 11 Config Management architecture 12 Integrations with CI/CD 13 Security and policies 14 Marketplace 15 Migrate 16 Breaking the monolith 17 Compute environment running on bare metal

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494

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Simon and Schuster

Published Date

ISBN 10

1638352127

ISBN 13

9781638352129

Deep Learning with TensorFlow and Keras

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

By: Amita Kapoor,Antonio Gulli,Sujit Pal,Francois Chollet

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Deep Learning with TensorFlow and Keras

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

By: Amita Kapoor,Antonio Gulli,Sujit Pal,Francois Chollet

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook Description Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using 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 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML. What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is for This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.

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699

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Packt Publishing Ltd

Published Date

ISBN 10

1803245719

ISBN 13

9781803245713

Transformers for Natural Language Processing

Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

By: Denis Rothman

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Transformers for Natural Language Processing

Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

By: Denis Rothman

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.What you will learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who this book is for If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!

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603

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Packt Publishing Ltd

Published Date

ISBN 10

1803243481

ISBN 13

9781803243481

Deep Learning for Natural Language Processing

A Gentle Introduction

By: Mihai Surdeanu,Marco Antonio Valenzuela-Escárcega

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Deep Learning for Natural Language Processing

A Gentle Introduction

By: Mihai Surdeanu,Marco Antonio Valenzuela-Escárcega

Provides a clear, accessible introduction to deep learning for natural language processing, covering both practical and theoretical aspects.

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345

Publisher

Cambridge University Press

Published Date

ISBN 10

1316515664

ISBN 13

9781316515662

Enactment, Politics, and Truth

Pauline Themes in Agamben, Badiou, and Heidegger

By: Antonio Cimino

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Enactment, Politics, and Truth

Pauline Themes in Agamben, Badiou, and Heidegger

By: Antonio Cimino

Enactment, Politics, and Truth explores the interpretations of Saint Paul by Giorgio Agamben, Alain Badiou, and Martin Heidegger. These interpretations are characterized by substantial thematic overlap that can be traced back to a key subject: the articulation of Pauline faith (pistis). Although each thinker approaches the issue from a different angle, they all interpret Pauline pistis by focusing on how it is enacted, articulated, and expressed in Saint Paul's concrete situation. Antonio Cimino sheds light on why Agamben, Badiou, and Heidegger address Pauline pistis and what kind of philosophical motives underlie their readings.

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190

Publisher

Bloomsbury Publishing USA

Published Date

ISBN 10

1501341030

ISBN 13

9781501341038

Five Albanian Villages

Guidelines for a Sustainable Tourism Development through the Enhancement of the Cultural Heritage

By: Antonio Laurìa,Valbona Flora,Kamela Guza

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Five Albanian Villages

Guidelines for a Sustainable Tourism Development through the Enhancement of the Cultural Heritage

By: Antonio Laurìa,Valbona Flora,Kamela Guza

This book is the result of a research project designed and carried out at the Department of Architecture, University of Florence. This research was based on the transfer of knowledge from members of the Albanian Diaspora in Italy (university students, young architects and researchers) to their home country. This unique process blazed a trail in the Albania-related studies by creating a methodology, which could be replicated not only in Albanian rural contexts, but also elsewhere. The book constitutes a structured tool for generating sustainable and socially inclusive territorial development processes in five lesser-known Albanian cultural sites. Their tangible and intangible cultural heritage was seen as a driving factor for triggering development processes aimed at improving the inhabitants’ quality of life and strengthening local identity and social networks. Through concrete proposals and strategies, the book offers scenarios and solutions capable of enhancing the potential of each village and, at the same time, counteracting the effects of land abandonment that so often characterise them.

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322

Publisher

Firenze University Press

Published Date

ISBN 10

8855181742

ISBN 13

9788855181747

Deep Learning with Keras

By: Antonio Gulli,Sujit Pal

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Deep Learning with Keras

By: Antonio Gulli,Sujit Pal

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.

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310

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Packt Publishing Ltd

Published Date

ISBN 10

1787129039

ISBN 13

9781787129030

Deep Learning with Keras

By: Antonio Gulli,Sujit Pal

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Deep Learning with Keras

By: Antonio Gulli,Sujit Pal

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310

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Packt Publishing Ltd

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ISBN 10

1787129039

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9781787129030

Deep Learning with TensorFlow and Keras

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

By: Amita Kapoor,Antonio Gulli,Sujit Pal,Francois Chollet

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Deep Learning with TensorFlow and Keras

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

By: Amita Kapoor,Antonio Gulli,Sujit Pal,Francois Chollet

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699

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Packt Publishing Ltd

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ISBN 10

1803245719

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9781803245713

TensorFlow 1.x Deep Learning Cookbook

Over 90 unique recipes to solve artificial-intelligence driven problems with Python

By: Antonio Gulli,Amita Kapoor

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TensorFlow 1.x Deep Learning Cookbook

Over 90 unique recipes to solve artificial-intelligence driven problems with Python

By: Antonio Gulli,Amita Kapoor

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526

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Packt Publishing Ltd

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ISBN 10

1788291867

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9781788291866

Deep Learning with TensorFlow 2 and Keras

Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

By: Antonio Gulli,Amita Kapoor,Sujit Pal

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Deep Learning with TensorFlow 2 and Keras

Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

By: Antonio Gulli,Amita Kapoor,Sujit Pal

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook 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 learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho 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. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

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647

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Packt Publishing Ltd

Published Date

ISBN 10

1838827722

ISBN 13

9781838827724

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli,Sujit Pal,Amita Kapoor

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Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli,Sujit Pal,Amita Kapoor

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.

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646

Published Date

ISBN 10

1838823417

ISBN 13

9781838823412

Google Anthos in Action

Manage hybrid and multi-cloud Kubernetes clusters

By: Antonio Gulli

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Google Anthos in Action

Manage hybrid and multi-cloud Kubernetes clusters

By: Antonio Gulli

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Solve jigsaw puzzle

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494

Publisher

Simon and Schuster

Published Date

ISBN 10

1638352127

ISBN 13

9781638352129

Book's by Antonio Gulli

TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook

Google Anthos in Action

Google Anthos in Action

Deep Learning with TensorFlow and Keras

Deep Learning with TensorFlow and Keras

Transformers for Natural Language Processing

Transformers for Natural Language Processing

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

Enactment, Politics, and Truth

Enactment, Politics, and Truth

Five Albanian Villages

Five Albanian Villages

Deep Learning with Keras

Deep Learning with Keras

Deep Learning with Keras

Deep Learning with Keras

Deep Learning with TensorFlow and Keras

Deep Learning with TensorFlow and Keras

TensorFlow 1.x Deep Learning Cookbook

TensorFlow 1.x Deep Learning Cookbook

Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras - Second Edition

Deep Learning with TensorFlow 2 and Keras - Second Edition

Google Anthos in Action

Google Anthos in Action

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