Getting Started with TensorFlow

  • Filename: getting-started-with-tensorflow.
  • ISBN: 9781786469069
  • Release Date: 2016-07-29
  • Number of pages: 180
  • Author: Giancarlo Zaccone
  • Publisher: Packt Publishing Ltd



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.

Machine Learning with Tensorflow

  • Filename: machine-learning-with-tensorflow.
  • ISBN: 1617293873
  • Release Date: 2017-01-31
  • Number of pages: 325
  • Author: Nishant Shukla
  • Publisher: Manning Publications



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.

FIRST CONTACT WITH TENSORFLOW

  • Filename: first-contact-with-tensorflow.
  • ISBN: 9781326569334
  • Release Date: 2012-04
  • Number of pages: 142
  • Author: Jordi Torres
  • Publisher: Lulu.com



The purpose of this book is to help to spreadTensorFlowknowledge among engineers who want to expand their wisdom in the exciting world of Machine Learning. We believe that anyone with an engineering background might require from now on Deep Learning, an"

Building Machine Learning Systems with Python

  • Filename: building-machine-learning-systems-with-python.
  • ISBN: 9781782161417
  • Release Date: 2013-01-01
  • Number of pages: 290
  • Author: Willi Richert
  • Publisher: Packt Publishing Ltd



This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.

Python Machine Learning

  • Filename: python-machine-learning.
  • ISBN: 9781783555147
  • Release Date: 2015-09-23
  • Number of pages: 454
  • Author: Sebastian Raschka
  • Publisher: Packt Publishing Ltd



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 in Action

  • Filename: machine-learning-in-action.
  • ISBN: 1617290181
  • Release Date: 2011-12
  • Number of pages: 354
  • Author: Peter Harrington
  • Publisher: Manning Publications



Provides information on the concepts of machine theory, covering such topics as statistical data processing, data visualization, and forecasting.

Learning OpenCV

  • Filename: learning-opencv.
  • ISBN: 9780596554040
  • Release Date: 2008-09-24
  • Number of pages: 580
  • Author: Gary Bradski
  • Publisher: "O'Reilly Media, Inc."



"This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised."-William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes: A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

Introduction to Statistical Machine Learning

  • Filename: introduction-to-statistical-machine-learning.
  • ISBN: 9780128023501
  • Release Date: 2015-10-31
  • Number of pages: 534
  • Author: Masashi Sugiyama
  • Publisher: Morgan Kaufmann



Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

Python for Data Science For Dummies

  • Filename: python-for-data-science-for-dummies.
  • ISBN: 9781118843987
  • Release Date: 2015-06-23
  • Number of pages: 432
  • Author: John Paul Mueller
  • Publisher: John Wiley & Sons



Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

Fundamentals of Deep Learning

  • Filename: fundamentals-of-deep-learning.
  • ISBN: 1491925612
  • Release Date: 2015-11-25
  • Number of pages: 150
  • Author: Nikhil Buduma
  • Publisher: O'Reilly Media



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.

How Google Tests Software

  • Filename: how-google-tests-software.
  • ISBN: 9780132851558
  • Release Date: 2012-03-21
  • Number of pages: 272
  • Author: James A. Whittaker
  • Publisher: Addison-Wesley



2012 Jolt Award finalist! Pioneering the Future of Software Test Do you need to get it right, too? Then, learn from Google. Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you’re not quite Google’s size…yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests…thinking like real users…implementing exploratory, black box, white box, and acceptance testing…getting usable feedback…tracking issues…choosing and creating tools…testing “Docs & Mocks,” interfaces, classes, modules, libraries, binaries, services, and infrastructure…reviewing code and refactoring…using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator–and make your whole organization more productive!

Learning Python

  • Filename: learning-python.
  • ISBN: 9781449355692
  • Release Date: 2013-06-12
  • Number of pages: 1600
  • Author: Mark Lutz
  • Publisher: "O'Reilly Media, Inc."



Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

Color Imaging

  • Filename: color-imaging.
  • ISBN: 9781568813448
  • Release Date: 2008-07-22
  • Number of pages: 1058
  • Author: Erik Reinhard
  • Publisher: A K Peters, Ltd.



This book provides the reader with an understanding of what color is, where color comes from, and how color can be used correctly in many different applications. The authors first treat the physics of light and its interaction with matter at the atomic level, so that the origins of color can be appreciated. The intimate relationship between energy levels, orbital states, and electromagnetic waves helps to explain why diamonds shimmer, rubies are red, and the feathers of the Blue Jay are blue. Then, color theory is explained from its origin to the current state of the art, including image capture and display as well as the practical use of color in disciplines such as computer graphics, computer vision, photography, and film.

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises

  • Filename: introduction-to-random-signals-and-applied-kalman-filtering-with-matlab-exercises.
  • ISBN: 0470609699
  • Release Date: 2012-02-07
  • Number of pages: 383
  • Author: Robert Grover Brown
  • Publisher: John Wiley & Sons



Machine generated contents note: PART 1: RANDOM SIGNALS BACKGROUND Chapter 1 Probability and Random Variables: A Review Chapter 2 Mathematical Description of Random Signals Chapter 3 Linear Systems Response, State-space Modeling and Monte Carlo Simulation PART 2: KALMAN FILTERING AND APPLICATIONS Chapter 4 Discrete Kalman Filter Basics Chapter 5 Intermediate Topics on Kalman Filtering Chapter 6 Smoothing and Further Intermediate Topics Chapter 7 Linearization, Nonlinear Filtering and Sampling Bayesian Filters Chapter 8 the "Go-Free" Concept, Complementary Filter and Aided Inertial Examples Chapter 9 Kalman Filter Applications to the GPS and Other Navigation Systems APPENDIX A. Laplace and Fourier Transforms APPENDIX B. The Continuous Kalman Filter.

Apache Mahout

  • Filename: apache-mahout.
  • ISBN: 1523775785
  • Release Date: 2016-02-18
  • Number of pages: 232
  • Author: Dmitriy Lyubimov
  • Publisher:



Apache Mahout: Beyond MapReduce. Distributed algorithm design This book is about designing mathematical and Machine Learning algorithms using the Apache Mahout "Samsara" platform. The material takes on best programming practices as well as conceptual approaches to attacking Machine Learning problems in big datasets. Math is explained, followed by code examples of distributed and in-memory computations. Written by Apache Mahout committers for people who want to learn how to design distributed math algorithms as well as how to use some of the new Mahout "Samsara" algorithms off-the-shelf. The book covers Apache Mahout 0.10 and 0.11.

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