Learn Tensorflow Enterprise

Author : KC Tung
Publisher : Packt Publishing Ltd
Page : 314 pages
File Size : 50,5 Mb
Release : 2020-11-27
Category : Computers
ISBN : 9781800204874

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Learn TensorFlow Enterprise by KC Tung Pdf

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.

Author : Matthew Scarpino
Publisher : John Wiley & Sons
Page : 368 pages
File Size : 55,9 Mb
Release : 2018-04-03
Category : Computers
ISBN : 9781119466215

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TensorFlow For Dummies by Matthew Scarpino Pdf

Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with TensorBoard Perform image recognition with convolutional neural networks (CNNs) Analyze sequential data with recurrent neural networks (RNNs) Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP) If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.

Author : Ankit Jain,Armando Fandango,Amita Kapoor
Publisher : Packt Publishing Ltd
Page : 311 pages
File Size : 47,6 Mb
Release : 2018-11-30
Category : Computers
ISBN : 9781789132403

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TensorFlow Machine Learning Projects by Ankit Jain,Armando Fandango,Amita Kapoor Pdf

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

Author : Antonio Gulli,Amita Kapoor,Sujit Pal
Publisher : Packt Publishing Ltd
Page : 647 pages
File Size : 49,8 Mb
Release : 2019-12-27
Category : Computers
ISBN : 9781838827724

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Deep Learning with TensorFlow 2 and Keras by Antonio Gulli,Amita Kapoor,Sujit Pal Pdf

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.

Author : Manpreet Singh Ghotra,Rajdeep Dua
Publisher : Packt Publishing Ltd
Page : 266 pages
File Size : 48,6 Mb
Release : 2017-11-10
Category : Computers
ISBN : 9781788397759

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Neural Network Programming with TensorFlow by Manpreet Singh Ghotra,Rajdeep Dua Pdf

Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs. Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you. Each concept is backed by a generic and real-world problem, followed by a variation, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.

Author : Nick McClure
Publisher : Packt Publishing Ltd
Page : 370 pages
File Size : 41,6 Mb
Release : 2017-02-14
Category : Computers
ISBN : 9781786466303

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TensorFlow Machine Learning Cookbook by Nick McClure Pdf

Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Author : KC Tung
Publisher : "O'Reilly Media, Inc."
Page : 214 pages
File Size : 45,8 Mb
Release : 2021-07-19
Category : Computers
ISBN : 9781492089131

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TensorFlow 2 Pocket Reference by KC Tung Pdf

This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in TensorFlow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
Page : 851 pages
File Size : 45,9 Mb
Release : 2019-09-05
Category : Computers
ISBN : 9781492032595

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Pdf

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

Author : Ankur A. Patel,Ajay Uppili Arasanipalai
Publisher : "O'Reilly Media, Inc."
Page : 330 pages
File Size : 53,5 Mb
Release : 2021-05-12
Category : Computers
ISBN : 9781492062523

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Applied Natural Language Processing in the Enterprise by Ankur A. Patel,Ajay Uppili Arasanipalai Pdf

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

Author : K. C. Tung
Publisher : O'Reilly Media
Page : 300 pages
File Size : 55,9 Mb
Release : 2021-11-16
Category : Electronic
ISBN : 1492089184

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TensorFlow 2 Pocket Reference by K. C. Tung Pdf

This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in TensorFlow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning

Author : Joseph Babcock,Raghav Bali
Publisher : Packt Publishing Ltd
Page : 489 pages
File Size : 55,5 Mb
Release : 2021-04-30
Category : Computers
ISBN : 9781800208506

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Generative AI with Python and TensorFlow 2 by Joseph Babcock,Raghav Bali Pdf

Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

Author : Adnan Masood,Adnan Hashmi
Publisher : Apress
Page : 437 pages
File Size : 51,8 Mb
Release : 2019-03-27
Category : Computers
ISBN : 9781484241066

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Cognitive Computing Recipes by Adnan Masood,Adnan Hashmi Pdf

Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you’ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem. What You Will LearnBuild production-ready solutions using Microsoft Cognitive Services APIs Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK) Solve enterprise problems in natural language processing and computer vision Discover the machine learning development life cycle – from formal problem definition to deployment at scale Who This Book Is For Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.

Author : Valliappa Lakshmanan,Martin Görner,Ryan Gillard
Publisher : "O'Reilly Media, Inc."
Page : 481 pages
File Size : 51,6 Mb
Release : 2021-07-21
Category : Computers
ISBN : 9781098102333

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Practical Machine Learning for Computer Vision by Valliappa Lakshmanan,Martin Görner,Ryan Gillard Pdf

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

Author : Jothi Periasamy
Publisher : Jothi Periasamy
Page : 305 pages
File Size : 55,5 Mb
Release : 2022-12-12
Category : Computers
ISBN : 8210379456XXX

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

Author : Jeff Tang
Publisher : Packt Publishing Ltd
Page : 396 pages
File Size : 49,5 Mb
Release : 2018-05-22
Category : Computers
ISBN : 9781788628808

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Intelligent Mobile Projects with TensorFlow by Jeff Tang Pdf

Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow Key Features Build TensorFlow-powered AI applications for mobile and embedded devices Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning Get practical insights and exclusive working code not available in the TensorFlow documentation Book Description As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips. What you will learn Classify images with transfer learning Detect objects and their locations Transform pictures with amazing art styles Understand simple speech commands Describe images in natural language Recognize drawing with Convolutional Neural Network and Long Short-Term Memory Predict stock price with Recurrent Neural Network in TensorFlow and Keras Generate and enhance images with generative adversarial networks Build AlphaZero-like mobile game app in TensorFlow and Keras Use TensorFlow Lite and Core ML on mobile Develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn Who this book is for If you're an iOS/Android developer interested in building and retraining others' TensorFlow models and running them in your mobile apps, or if you're a TensorFlow developer and want to run your new and amazing TensorFlow models on mobile devices, this book is for you. You'll also benefit from this book if you're interested in TensorFlow Lite, Core ML, or TensorFlow on Raspberry Pi.