Hands On Machine Learning with Scikit Learn and TensorFlow

Hands On Machine Learning with Scikit Learn and TensorFlow Author Aurélien Géron
ISBN-10 9781491962268
Year 2017-03-13
Pages 566
Language en
Publisher "O'Reilly Media, Inc."
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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 Apply practical code examples without acquiring excessive machine learning theory or algorithm details

TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook Author Nick McClure
ISBN-10 9781786466303
Year 2017-02-14
Pages 370
Language en
Publisher Packt Publishing Ltd
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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.

Mastering Machine Learning with scikit learn

Mastering Machine Learning with scikit learn Author Gavin Hackeling
ISBN-10 9781783988372
Year 2014-10-29
Pages 238
Language en
Publisher Packt Publishing Ltd
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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 you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

Learning scikit learn Machine Learning in Python

Learning scikit learn  Machine Learning in Python Author Raul Garreta
ISBN-10 9781783281947
Year 2013-11-25
Pages 100
Language en
Publisher Packt Publishing Ltd
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The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Introduction to Machine Learning with Python

Introduction to Machine Learning with Python Author Andreas C. Müller
ISBN-10 9781449369903
Year 2016-09-26
Pages 394
Language en
Publisher "O'Reilly Media, Inc."
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Python Machine Learning

Python Machine Learning Author Sebastian Raschka
ISBN-10 9781783555147
Year 2015-09-23
Pages 454
Language en
Publisher Packt Publishing Ltd
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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.

Machine Learning with Tensorflow

Machine Learning with Tensorflow Author Nishant Shukla
ISBN-10 1617293873
Year 2017-01-31
Pages 325
Language en
Publisher Manning Publications
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Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world.

Practical Machine Learning with H2O

Practical Machine Learning with H2O Author Darren Cook
ISBN-10 9781491964576
Year 2016-12-05
Pages 300
Language en
Publisher "O'Reilly Media, Inc."
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Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

Deep Learning

Deep Learning Author Ian Goodfellow
ISBN-10 9780262035613
Year 2016-11-18
Pages 800
Language en
Publisher MIT Press
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Getting Started with TensorFlow

Getting Started with TensorFlow Author Giancarlo Zaccone
ISBN-10 9781786469069
Year 2016-07-29
Pages 180
Language en
Publisher Packt Publishing Ltd
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Get up and running with the latest numerical computing library by Google and dive deeper into your data! About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow! Dive into the next generation of numerical computing and get the most out of your data with this quick guide Who This Book Is For This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful. What You Will Learn Install and adopt TensorFlow in your Python environment to solve mathematical problems Get to know the basic machine and deep learning concepts Train and test neural networks to fit your data model Make predictions using regression algorithms Analyze your data with a clustering procedure Develop algorithms for clustering and data classification Use GPU computing to analyze big data In Detail Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application. Style and approach Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.

Fundamentals of Deep Learning

Fundamentals of Deep Learning Author Nikhil Buduma
ISBN-10 9781491925584
Year 2017-05-25
Pages 304
Language en
Publisher "O'Reilly Media, Inc."
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With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This 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 teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.

Deep Learning with Python

Deep Learning with Python Author Nikhil Ketkar
ISBN-10 9781484227664
Year 2017-04-18
Pages 226
Language en
Publisher Apress
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Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Building Machine Learning Projects with TensorFlow

Building Machine Learning Projects with TensorFlow Author Rodolfo Bonnin
ISBN-10 9781786466822
Year 2016-11-24
Pages 282
Language en
Publisher Packt Publishing Ltd
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Engaging projects that will teach you how complex data can be exploited to gain the most insight About This Book Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow's features from the very beginning. Who This Book Is For This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected. What You Will Learn Load, interact, dissect, process, and save complex datasets Solve classification and regression problems using state of the art techniques Predict the outcome of a simple time series using Linear Regression modeling Use a Logistic Regression scheme to predict the future result of a time series Classify images using deep neural network schemes Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer Resolve character recognition problems using the Recurrent Neural Network (RNN) model In Detail This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each 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 Tensors. 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 approach This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine learning and numerical computation – a must-have for your bookshelf!