Hands-on machine learning with scikit-learn, keras, and tensorflow 3e, Geron, Aurelien
Старое издание
Автор: Aurelein Geron Название: Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques for Building Intelligent Systems ISBN: 1491962291 ISBN-13(EAN): 9781491962299 Издательство: Wiley Цена: 7602.00 р. Наличие на складе: Невозможна поставка. Описание: 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.
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.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help 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.
Описание: Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
Описание: In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow.
Автор: Gulli Antonio, Pal Sujit, Kapoor Amita Название: Deep Learning with TensorFlow 2 and Keras - Second Edition ISBN: 1838823417 ISBN-13(EAN): 9781838823412 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ITIL(R) 4 Managing Professional CoursewareITIL 4 Managing Professional (ITIL MP) consists of four modules and is the next level of ITIL 4 to be released after ITIL 4 Foundation. ITIL MP targets IT practitioners working within technology and digital teams across businesses. The Managing Professional (MP) stream provides practical and technical knowledge about how to run successful IT-enabled services, teams and workflows. The Managing Professional Transition module is designed to allow ITIL v3 candidates to easily transition to ITIL 4. They can get the ITIL 4 Managing Professional designation through one course and one exam. The material includes;¢ Updated glossaries with highlighted changes for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ New diagram packs with annotations for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ Updated syllabi with highlighted changes for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ The `High Velocity IT` manuscript, syllabus, glossary and diagram pack remain unchanged¢ Practices Overviews and the product brochure remain unchanged¢ An up-to-date Quick Reference Guide with all the information you need¢ Updated Core Manuscripts, for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ While the changes are quite large, they have been made for streamlining or refinement only¢ There have been no changes to the examinable content or the key concepts
Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines.
Understand the machine learning management lifecycle
Implement data pipelines with Apache Airflow and Kubeflow Pipelines
Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform
Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement
Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js
Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated
Design model feedback loops to increase your data sets and learn when to update your machine learning models
Описание: Learn how to solve challenging machine learning problems with TensorFlow, Google`s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals.
One-stop solution for NLP practitioners, ML developers and data scientists to build effective NLP systems that can perform real-world complicated tasks
Key Features
Implement deep learning algorithms such as BiLSTMS, CRFs, and many more using TensorFlow 2
Explore classical NLP techniques and libraries including parts-of-speech tagging and tokenization
Learn practical applications of NLP covering the forefronts of the field like sentiment analysis and generating text
Book Description
In the last couple of years, there have been tremendous advances in natural language processing, and we are now moving from research labs into practical applications. Advanced Natural Language Processing comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques.
This book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It goes into the details of applying the concepts of text pre-processing using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. Named Entity Recognition (NER), a cornerstone of task-oriented bots, is built from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs.
Taking a practical and application-focused perspective, the book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbot design. It also covers one of the most important reasons behind recent advances in NLP - applying transfer learning and fine-tuning using TensorFlow 2.
Further, it covers practical techniques that can simplify the labelling of textual data which otherwise proves to be a costly affair. The book also has a working code for each tech piece so that you can adapt them to your use cases.
By the end of this TensorFlow book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
What You Will Learn
Grasp important pre-steps in building NLP applications like POS tagging
Deal with vast amounts of unlabeled and small labelled Datasets in NLP
Use transfer and weakly supervised learning using libraries like Snorkel
Perform sentiment analysis using BERT
Apply encoder-decoder NN architectures and beam search for summarizing text
Use transformer models with attention to bring images and text together
Build applications that generate captions and answer questions about images
Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest deep NLP models
Who this book is for
This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.
The readers who can benefit the most from this book include:
Intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques
Professionals who already use TensorFlow/Python for purposes such as data science, ML, research, and analysis
Автор: Atienza, Rowel Название: Advanced deep learning with tensorflow 2 and keras - ISBN: 1838821651 ISBN-13(EAN): 9781838821654 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A second edition of the bestselling guide to exploring and mastering deep learning with Keras, updated to include TensorFlow 2.x with new chapters on object detection, semantic segmentation, and unsupervised learning using mutual information.
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
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Автор: Ganegedara Thushan Название: Natural Language Processing with Tensorflow ISBN: 1788478312 ISBN-13(EAN): 9781788478311 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today`s vast amount of unstructured data.
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