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HandsOn Machine Learning With ScikitLearn And TensorFlow

 
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MessagePosté le: Jeu 6 Juil - 13:39 (2017)    Sujet du message: HandsOn Machine Learning With ScikitLearn And TensorFlow Répondre en citant





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.
<ul><li>Explore the machine learning landscape, particularly neural nets</li><li>Use scikit-learn to track an example machine-learning project end-to-end</li><li>Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods</li><li>Use the TensorFlow library to build and train neural nets</li><li>Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning</li><li>Learn techniques for training and scaling deep neural nets</li><li>Apply practical code examples without acquiring excessive machine learning theory or algorithm details</li></ul>







Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron






Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production.Style and approachThis book is a practical guide to implementing TensorFlow in productionQuestions? See our FAQ or contact customer service: 1-800-889-8969 / 707-827-7019supportoreilly.com Download Example Code View/Submit Errata Graphics in this book are printed in black and white2 UAH428.28 UAH299.80 Pre-ordered Mastering Machine Learning with scikit-learn Gavin Hackeling October 29, 2014 2 UAH321.18 UAH224.83 If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for youThis book uses exposition and examples to help you understand major concepts in this complicated field.Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teamsWith exercises in each chapter to help you apply what youve learned, all you need is programming experience to get startedGenres Biographies & Memoirs Business & Investing Children's Books Computers & Technology Cooking & Housekeeping Fiction & Literature Health & Sports History Mystery & Thrillers Romance Science Fiction & Fantasy Home Top Charts New Arrivals Settings Android Device Manager Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.What You Will LearnSet up your data science toolbox using a Python scientific environment on Windows, Mac, and LinuxGet data ready for your data science projectManipulate, fix, and explore data in order to solve data science problemsSet up an experimental pipeline to test your data science hypothesesChoose the most effective and scalable learning algorithm for your data science tasksOptimize your machine learning models to get the best performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataIn DetailFully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using PythonData scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiencyThrough a series of recent breakthroughs, deep learning has boosted the entire field of machine learningEach project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with TensorsMachine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalabilityThis practical book shows you how00:56[PDF] Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques23 days agodailymotion.comYoull learn a range of techniques, starting with simple linear regression and progressing to deep neural networksThe proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis.Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website.This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practiceBy using concrete examples, minimal theory, and two production-ready Python frameworksscikit-learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systemsPython 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.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 modelsBeing 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 marketplace00:49Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to3 months agodailymotion.comThe machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package 07f867cfac



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