Learn Tensorflow Enterprise

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Learn TensorFlow Enterprise

Author : KC Tung
Publisher : Packt Publishing Ltd
ISBN 13 : 1800204876
Total Pages : 314 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Learn TensorFlow Enterprise by : KC Tung

Download or read book Learn TensorFlow Enterprise written by KC Tung and published by Packt Publishing Ltd. This book was released on 2020-11-27 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable enterprise-level deployment Key FeaturesBuild scalable, seamless, and enterprise-ready cloud-based machine learning applications using TensorFlow EnterpriseDiscover how to accelerate the machine learning development life cycle using enterprise-grade servicesManage Google’s cloud services to scale and optimize AI models in productionBook Description TensorFlow as a machine learning (ML) library has matured into a production-ready ecosystem. This beginner’s book uses practical examples to enable you to build and deploy TensorFlow models using optimal settings that ensure long-term support without having to worry about library deprecation or being left behind when it comes to bug fixes or workarounds. The book begins by showing you how to refine your TensorFlow project and set it up for enterprise-level deployment. You’ll then learn how to choose a future-proof version of TensorFlow. As you advance, you’ll find out how to build and deploy models in a robust and stable environment by following recommended practices made available in TensorFlow Enterprise. This book also teaches you how to manage your services better and enhance the performance and reliability of your artificial intelligence (AI) applications. You’ll discover how to use various enterprise-ready services to accelerate your ML and AI workflows on Google Cloud Platform (GCP). Finally, you’ll scale your ML models and handle heavy workloads across CPUs, GPUs, and Cloud TPUs. By the end of this TensorFlow book, you’ll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment. What you will learnDiscover how to set up a GCP TensorFlow Enterprise cloud instance and environmentHandle and format raw data that can be consumed by the TensorFlow model training processDevelop ML models and leverage prebuilt models using the TensorFlow Enterprise APIUse distributed training strategies and implement hyperparameter tuning to scale and improve your model training experimentsScale the training process by using GPU and TPU clustersAdopt the latest model optimization techniques and deployment methodologies to improve model efficiencyWho this book is for This book is for data scientists, machine learning developers or engineers, and cloud practitioners who want to learn and implement various services and features offered by TensorFlow Enterprise from scratch. Basic knowledge of the machine learning development process will be useful.

Deep Learning with TensorFlow 2 and Keras

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1838827722
Total Pages : 647 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Deep Learning with TensorFlow 2 and Keras by : Antonio Gulli

Download or read book Deep Learning with TensorFlow 2 and Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2019-12-27 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Practical Machine Learning for Computer Vision

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098102339
Total Pages : 481 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149203259X
Total Pages : 851 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by : Aurélien Géron

Download or read book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

GOOGLE CLOUD PLATFORM FOR ENTERPRISE MLOPS:A PRACTICAL GUIDE TO CLOUD COMPUTING: PART ONE

Author : Jothi Periasamy
Publisher : Jothi Periasamy
ISBN 13 :
Total Pages : 305 pages
Book Rating : 4./5 ( download)

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Book Synopsis GOOGLE CLOUD PLATFORM FOR ENTERPRISE MLOPS:A PRACTICAL GUIDE TO CLOUD COMPUTING: PART ONE by : Jothi Periasamy

Download or read book GOOGLE CLOUD PLATFORM FOR ENTERPRISE MLOPS:A PRACTICAL GUIDE TO CLOUD COMPUTING: PART ONE written by Jothi Periasamy and published by Jothi Periasamy. This book was released on 2022-12-12 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Predictive Analytics for the Modern Enterprise

Author : Nooruddin Abbas Ali
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098136829
Total Pages : 358 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Predictive Analytics for the Modern Enterprise by : Nooruddin Abbas Ali

Download or read book Predictive Analytics for the Modern Enterprise written by Nooruddin Abbas Ali and published by "O'Reilly Media, Inc.". This book was released on 2024-05-20 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

Deep Learning with Python

Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Python by : Francois Chollet

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Learning TensorFlow.js

Author : Gant Laborde
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149209076X
Total Pages : 342 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Learning TensorFlow.js by : Gant Laborde

Download or read book Learning TensorFlow.js written by Gant Laborde and published by "O'Reilly Media, Inc.". This book was released on 2021-05-10 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch

Google Cloud AI and Machine Learning Certification

Author :
Publisher : Cybellium
ISBN 13 : 1836798164
Total Pages : 228 pages
Book Rating : 4.8/5 (367 download)

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Book Synopsis Google Cloud AI and Machine Learning Certification by :

Download or read book Google Cloud AI and Machine Learning Certification written by and published by Cybellium . This book was released on 2024-10-26 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

Python Machine Learning

Author : Sebastian Raschka
Publisher : Packt Publishing Ltd
ISBN 13 : 1783555149
Total Pages : 455 pages
Book Rating : 4.7/5 (835 download)

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Book Synopsis Python Machine Learning by : Sebastian Raschka

Download or read book Python Machine Learning written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2015-09-23 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

Enterprise AI in the Cloud

Author : Rabi Jay
Publisher : John Wiley & Sons
ISBN 13 : 1394213069
Total Pages : 763 pages
Book Rating : 4.3/5 (942 download)

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Book Synopsis Enterprise AI in the Cloud by : Rabi Jay

Download or read book Enterprise AI in the Cloud written by Rabi Jay and published by John Wiley & Sons. This book was released on 2023-12-20 with total page 763 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embrace emerging AI trends and integrate your operations with cutting-edge solutions Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You’ll also discover best practices on optimizing cloud infrastructure for scalability and automation. Enterprise AI in the Cloud helps you gain a solid understanding of: AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.

Google Certification Guide - Google Professional Machine Learning Engineer

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 171 pages
Book Rating : 4.8/5 (714 download)

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Book Synopsis Google Certification Guide - Google Professional Machine Learning Engineer by : Cybellium Ltd

Download or read book Google Certification Guide - Google Professional Machine Learning Engineer written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Google Certification Guide - Google Professional Machine Learning Engineer Unlock the World of Machine Learning on Google Cloud Embark on a transformative journey to become a Google Professional Machine Learning Engineer with this comprehensive guide. Designed for those who aspire to master the application of machine learning techniques and tools in the Google Cloud environment, this book is an essential resource for professionals seeking to harness the power of ML in their projects and workflows. What Awaits Inside: Advanced ML Concepts and Practices: Dive deep into the world of machine learning on Google Cloud, covering services like AI Platform, TensorFlow, and BigQuery ML. Real-World Applications: Learn through practical scenarios and hands-on examples, illustrating the effective implementation of machine learning models and solutions on Google Cloud. Strategic Exam Preparation: Gain crucial insights into the certification exam's structure and content, complemented by comprehensive practice questions and preparation strategies. Cutting-Edge ML Trends: Stay updated with the latest advancements in Google Cloud machine learning technologies, ensuring your skills remain relevant and innovative. Authored by a Machine Learning Expert Written by an experienced practitioner in the field of machine learning on Google Cloud, this guide bridges the gap between theoretical knowledge and practical application, offering a rich and comprehensive learning experience. Your Comprehensive Guide to ML Certification Whether you’re an experienced machine learning engineer or looking to elevate your expertise in Google Cloud's ML offerings, this book is a valuable companion, guiding you through the intricacies of machine learning in Google Cloud and preparing you for the Professional Machine Learning Engineer certification. Elevate Your Machine Learning Journey This guide is more than a pathway to certification; it's a deep dive into the practical and innovative aspects of machine learning in the Google Cloud environment, designed to equip you with the skills and knowledge for a thriving career in this dynamic field. Begin Your Machine Learning Adventure Start your journey to becoming a certified Google Professional Machine Learning Engineer. This guide is not just about passing an exam; it's about unlocking new opportunities and frontiers in the exciting world of machine learning on Google Cloud. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Learning Google Cloud Vertex AI

Author : Hemanth Kumar K
Publisher : BPB Publications
ISBN 13 : 9355515359
Total Pages : 308 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Learning Google Cloud Vertex AI by : Hemanth Kumar K

Download or read book Learning Google Cloud Vertex AI written by Hemanth Kumar K and published by BPB Publications. This book was released on 2023-08-28 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build an end-to-end data to AI solution on Google Cloud using Vertex AI KEY FEATURES ● Harness the power of AutoML capabilities to build machine learning models. ● Learn how to train custom machine learning models on the Google Cloud Platform. ● Accelerate your career in data analytics by leveraging the capabilities of GCP. DESCRIPTION Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By the time you finish reading this book, you will be able to navigate Vertex AI proficiently, even if you lack prior experience with cloud platforms. With the inclusion of numerous code examples throughout the book, you will be equipped with the necessary skills and confidence to create machine learning solutions using Vertex AI. WHAT YOU WILL LEARN ● Learn how to create projects, store data in GCP, and manage access permissions effectively. ● Discover how AutoML can be utilized for streamlining workflows. ● Learn how to construct pipelines using TFX (TensorFlow Extended) and Kubeflow components. ● Gain an overview of the purpose and significance of the Feature Store. ● Explore the concept of explainable AI and its role in understanding machine learning models. WHO THIS BOOK IS FOR This book is designed for data scientists and advanced AI practitioners who are interested in learning how to perform machine learning tasks on the Google Cloud Platform. Having prior knowledge of machine learning concepts and proficiency in Python programming would greatly benefit readers. TABLE OF CONTENTS 1. Basics of Google Cloud Platform 2. Introduction to Vertex AI and AutoML Tabular 3. AutoML Image, Text, and Pre-built Models 4. Vertex AI Workbench and Custom Model Training 5. Vertex AI Custom Model Hyperparameter and Deployment 6. Introduction to Pipelines and Kubeflow 7. Pipelines using Kubeflow for Custom Models 8. Pipelines using TensorFlow Extended 9. Vertex AI Feature Store 10. Explainable AI

TensorFlow Machine Learning Projects

Author : Ankit Jain
Publisher : Packt Publishing Ltd
ISBN 13 : 1789132401
Total Pages : 311 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis TensorFlow Machine Learning Projects by : Ankit Jain

Download or read book TensorFlow Machine Learning Projects written by Ankit Jain and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques

TensorFlow 2 Reinforcement Learning Cookbook

Author : Praveen Palanisamy
Publisher : Packt Publishing Ltd
ISBN 13 : 1838985999
Total Pages : 473 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis TensorFlow 2 Reinforcement Learning Cookbook by : Praveen Palanisamy

Download or read book TensorFlow 2 Reinforcement Learning Cookbook written by Praveen Palanisamy and published by Packt Publishing Ltd. This book was released on 2021-01-15 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook Description With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch. What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is for The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment

Author : Peter Jones
Publisher : Walzone Press
ISBN 13 :
Total Pages : 202 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment by : Peter Jones

Download or read book Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment written by Peter Jones and published by Walzone Press. This book was released on 2024-10-11 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the realm of artificial intelligence with "Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment." This all-encompassing guide provides an in-depth understanding of AI, machine learning, and deep learning, powered by TensorFlow—Google's leading AI framework. Whether you're a beginner starting your AI journey or a professional looking to elevate your expertise in AI model deployment, this book is tailored to meet your needs. Covering crucial topics like neural network design, convolutional and recurrent neural networks, natural language processing, and computer vision, it offers a robust introduction to TensorFlow and its AI applications. Through hands-on examples and a focus on practical solutions, you'll learn how to apply TensorFlow to solve real-world challenges. From theoretical foundations to deployment techniques, "Mastering Deep Learning with TensorFlow" takes you through every step, preparing you to build, fine-tune, and deploy advanced AI models. By the end, you’ll be ready to harness TensorFlow’s full potential, making strides in the rapidly evolving field of artificial intelligence. This book is an indispensable resource for anyone eager to engage with or advance in AI.

Architecting Google Cloud Solutions

Author : Victor Dantas
Publisher : Packt Publishing Ltd
ISBN 13 : 1800564155
Total Pages : 472 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Architecting Google Cloud Solutions by : Victor Dantas

Download or read book Architecting Google Cloud Solutions written by Victor Dantas and published by Packt Publishing Ltd. This book was released on 2021-05-14 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Achieve your business goals and build highly available, scalable, and secure cloud infrastructure by designing robust and cost-effective solutions as a Google Cloud Architect. Key FeaturesGain hands-on experience in designing and managing high-performance cloud solutionsLeverage Google Cloud Platform to optimize technical and business processes using cutting-edge technologies and servicesUse Google Cloud Big Data, AI, and ML services to design scalable and intelligent data solutionsBook Description Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs. You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance. By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform. What you will learnGet to grips with compute, storage, networking, data analytics, and pricingDiscover delivery models such as IaaS, PaaS, and SaaSExplore the underlying technologies and economics of cloud computingDesign for scalability, business continuity, observability, and resiliencySecure Google Cloud solutions and ensure complianceUnderstand operational best practices and learn how to architect a monitoring solutionGain insights into modern application design with Google CloudLeverage big data, machine learning, and AI with Google CloudWho this book is for This book is for cloud architects who are responsible for designing and managing cloud solutions with GCP. You'll also find the book useful if you're a system engineer or enterprise architect looking to learn how to design solutions with Google Cloud. Moreover, cloud architects who already have experience with other cloud providers and are now beginning to work with Google Cloud will benefit from the book. Although an intermediate-level understanding of cloud computing and distributed apps is required, prior experience of working in the public and hybrid cloud domain is not mandatory.