Learn Tensorflow Enterprise
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Learn Tensorflow Enterprise
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Author : KC Tung
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-11-27
Learn Tensorflow Enterprise written by KC Tung and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-27 with Computers categories.
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.
Learn Tensorflow
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2024-12-12
Learn Tensorflow written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Business & Economics categories.
LEARN TENSORFLOW Master AI Model Development with Scalability and Precision. From Fundamentals to Practical Applications. This comprehensive guide is aimed at developers and students who want to create robust, high-performance, and scalable solutions with TensorFlow. You will learn to apply deep learning efficiently, master data pipelines, build advanced models, and deploy them professionally into production. Includes: • Tensor manipulation and model structuring with Keras • Building and training CNNs, RNNs, Transformers, and GANs • Regularization techniques, hyperparameter tuning, and performance optimization • Practical implementation with tf.data, TensorBoard, and TensorFlow Lite • Deployment with TensorFlow Serving, IoT integration, and use of GPUs and TPUs • Real-world cases in NLP, computer vision, healthcare, and enterprise systems By the end, you'll be fully equipped to develop TensorFlow applications for critical scenarios and scalable environments with technical excellence. tensorflow, keras, deep learning, cnn, rnn, gpu, deployment, iot, scalable models
Considering Tensorflow For The Enterprise
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Author : Sean Patrick Murphy
language : en
Publisher:
Release Date : 2017
Considering Tensorflow For The Enterprise written by Sean Patrick Murphy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Artificial intelligence categories.
Tensorflow 2 Pocket Reference
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Author : KC Tung
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-07-19
Tensorflow 2 Pocket Reference written by KC Tung and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Computers categories.
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
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Cognitive Computing Recipes
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Author : Adnan Masood
language : en
Publisher: Apress
Release Date : 2019-03-27
Cognitive Computing Recipes written by Adnan Masood and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-27 with Computers categories.
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 Learn Build 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.
Tensorflow 2 Pocket Reference
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Author : K. C. Tung
language : en
Publisher: O'Reilly Media
Release Date : 2021-11-16
Tensorflow 2 Pocket Reference written by K. C. Tung and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with categories.
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
Python For Programmers
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Author : Paul Deitel
language : en
Publisher: Prentice Hall
Release Date : 2019-03-15
Python For Programmers written by Paul Deitel and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Computers categories.
The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Accompanying code examples are available here: http://ptgmedia.pearsoncmg.com/imprint_downloads/informit/bookreg/9780135224335/9780135224335_examples.zip. Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.
Learn Vertex Ai
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-06-27
Learn Vertex Ai written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-27 with Business & Economics categories.
LEARN VERTEX AI Implement Enterprise AI on Google Cloud This book is aimed at technology professionals, data engineers, and students who want to master the use of Vertex AI for creating, automating, and governing artificial intelligence projects in corporate Google Cloud environments. Learn how to structure machine learning pipelines, integrate data, automate deployment and versioning processes, monitor performance, and implement MLOps and DataOps practices with security, scalability, and compliance. Explore practical integrations with BigQuery, Dataflow, Pub/Sub, Cloud Storage, as well as leading frameworks such as TensorFlow, PyTorch, and scikit-learn. Develop skills in multi-cloud deployment, model tuning, cost control, CI/CD automation, and complete governance of the data and model lifecycle. • Professional setup of Vertex AI on Google Cloud • Building automated and scalable machine learning pipelines • Advanced integration with BigQuery, Dataflow, Pub/Sub, and Cloud Storage • Deployment, versioning, and monitoring of production models • Orchestration with TensorFlow, PyTorch, scikit-learn, AutoML, and containers • CI/CD automation, performance tuning, cost control • Implementation of Feature Store, Model Registry, and access policies • Governance, auditing, compliance, and data security in AI • MLOps, DataOps strategies, and multi-cloud integration • Real-world applications, certification preparation, and critical projects Master Vertex AI and become a reference in corporate AI, delivering scalable, auditable projects aligned with global market demands. vertex ai, google cloud, machine learning, nvidia, pipelines, automation, bigquery, dataflow, pub/sub, cloud storage, ci/cd, mlops, automl, tensorflow, pytorch, feature store, model registry, dataops, model deployment, orchestration, monitoring, governance, data security
Hands On Artificial Intelligence With Tensorflow
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Author : Amir Ziai
language : en
Publisher:
Release Date : 2018-10-31
Hands On Artificial Intelligence With Tensorflow written by Amir Ziai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Artificial intelligence categories.
Book Description Artificial Intelligence (AI) is a popular area with an emphasis on creating intelligent machines that can reason, evaluate, and understand the same way as humans. It is used extensively across many fields, such as image recognition, robotics, language processing, healthcare, finance, and more. Hands-On Artificial Intelligence with TensorFlow gives you a rundown of essential AI concepts and their implementation with TensorFlow, also highlighting different approaches to solving AI problems using machine learning and deep learning techniques. In addition to this, the book covers advanced concepts, such as reinforcement learning, generative adversarial networks (GANs), and multimodal learning. Once you have grasped all this, you'll move on to exploring GPU computing and neuromorphic computing, along with the latest trends in quantum computing. You'll work through case studies that will help you examine AI applications in the important areas of computer vision, healthcare, and FinTech, and analyze their datasets. In the concluding chapters, you'll briefly investigate possible developments in AI that we can expect to see in the future. By the end of this book, you will be well-versed with the essential concepts of AI and their implementation using TensorFlow. What you will learn Explore the core concepts of AI and its different approaches Use the TensorFlow framework for smart applications Implement various machine and deep learning algorithms with TensorFlow Design self-learning RL systems and implement generative models Perform GPU computing efficiently using best practices Build enterprise-grade apps for computer vision, NLP, and healthcare Who this book is for Hands-On Artificial Intelligence with TensorFlow is for you if you are a machine learning developer, data scientist, AI researcher, or anyone who wants to build artificial intelligence applications using TensorFlow. You need to have some working knowledge of machine learning to get the most out of this book.