When you want to find machine learning murphy, you may need to consider between many choices. Finding the best machine learning murphy is not an easy task. In this post, we create a very short list about top 10 the best machine learning murphy for you. You can check detail product features, product specifications and also our voting for each product. Let’s start with following top 10 machine learning murphy:
Reviews
1. Machine Learning in Radiation Oncology: Theory and Applications
Description
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.2. Deep Learning (Adaptive Computation and Machine Learning series)
Feature
Deep LearningDescription
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.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
-- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
3. No Stress Tech Guide To Business Objects Crystal Reports 2008 For Beginners
Feature
Used Book in Good ConditionDescription
(A new version is available for Crystal Reports 2016. See ISBN 978-1935208358) If you have been looking for a beginners book that has a lot of easy to understand, step-by-step instructions and screen shots that show you how to complete and master Crystal Reports 2008 design techniques correctly, this is the book for you. The No Stress Tech Guide To Business Objects Crystal Reports 2008 For Beginners book, is a self-paced visual guide to learning Crystal Reports and is written from the perspective that the reader has not created a report before or has not used Crystal Reports. This book is for the beginner and intermediate user. To help you become familiar with the options and features, this book contains over 500 illustrations that provide a visual tour of the software. If you have used a previous version of Crystal Reports and only want to learn about the new features, see ISBN 1-935208-01-2 What's New in Crystal Reports 2008. If you are looking for a book for Crystal Reports Basic for Visual Studio, see ISBN 978-1935208198.4. Klutz LEGO Chain Reactions Craft Kit
Feature
NAPPA Silver Award WinnerDesign and build 10 amazing moving machines - teach your bricks new tricks
Comes with 80 page instructions, 33 LEGO pieces, instructions for 10 modules, 6 plastic balls, string, paper ramps and other components
Includes a 80 page instructional book with Klutz certified crystal-clear instructions
Includes more than 30 essential Lego elements
Recommended for children ages 8+
Description
Our book includes 33 special LEGO elements that combine with basic bricks from your collection to make your machines go. But dont worry that you wont have the right bricks; we worked with the folks at LEGO to make sure youll need only the most common bricks, and that there are plenty of substitutes. The result is a chain reaction of fun, as one thing leads to another and another and another.
Comes with: 78 page book, 33 LEGO elements, 6 LEGO balls, 6 feet of string, 8 paper ramps, 2 paper pop-up signs, 1 paper funnel ramp, 1 paper flag, 1 paper bucket, 1 platform
5. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Feature
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.Description
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketingin a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It isa valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
6. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Feature
The Machine Learning A Probabilistic PerspectiveDescription
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
7. Practical Data Science Cookbook - Real-World Data Science Projects to Help You Get Your Hands On Your Data
Feature
Practical Data Science CookbookDescription
Key Features
- Learn how to tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize data
- Get beyond the theory with real-world projects
- Expand your numerical programming skills through step-by-step code examples and learn more about the robust features of R and Python
Book Description
Data's value has grown exponentially in the past decade, with 'Big Data' today being one of the biggest buzzwords in business and IT, and data scientist hailed as 'the sexiest job of the 21st century'. Practical Data Science Cookbook helps you see beyond the hype and get past the theory by providing you with a hands-on exploration of data science. With a comprehensive range of recipes designed to help you learn fundamental data science tasks, you'll uncover practical steps to help you produce powerful insights into Big Data using R and Python.
Use this valuable data science book to discover tricks and techniques to get to grips with your data. Learn effective data visualization with an automobile fuel efficiency data project, analyze football statistics, learn how to create data simulations, and get to grips with stock market data to learn data modelling. Find out how to produce sharp insights into social media data by following data science tutorials that demonstrate the best ways to tackle Twitter data, and uncover recipes that will help you dive in and explore Big Data through movie recommendation databases.
Practical Data Science Cookbook is your essential companion to the real-world challenges of working with data, created to give you a deeper insight into a world of Big Data that promises to keep growing.
What you will learn
- Follow the recipes in this essential data science cookbook to learn the fundamentals of data science and data analysis
- Put theory into practice with a selection of real-world Big Data projects
- Learn the data science pipeline and successfully structure your data science project
- Find out how to clean, munge, and manipulate data
- Learn different approaches to data modelling and how to determine the most appropriate for your data
- Learn numerical computing with NumPy and SciPy
About the Authors
Tony Ojeda is the founder of District Data Labs, a cofounder of Data Community DC, and is actively involved in promoting data science education through both organizations.
Sean Patrick Murphy spent 15 years as a senior scientist at The Johns Hopkins University Applied Physics Laboratory, where he focused on machine learning, modeling and simulation, signal processing, and high performance computing in the Cloud. Now, he acts as an advisor and data consultant for companies in SF, NY, and DC.
Benjamin Bengfort has worked in military, industry, and academia for the past 8 years. He is currently pursuing his PhD in Computer Science at the University of Maryland, College Park, researching Metacognition and Natural Language Processing.
Abhijit Dasgupta is a data consultant working in the greater DC-Maryland-Virginia area, with several years of experience in biomedical consulting, business analytics, bioinformatics, and bioengineering consulting.
Table of Contents
- Preparing Your Data Science Environment
- Driving Visual Analysis with Automobile Data (R)
- Simulating American Football Data (R)
- Modeling Stock Market Data (R)
- Visually Exploring Employment Data (R)
- Creating Application-oriented Analyses Using Tax Data (Python)
- Driving Visual Analyses with Automobile Data (Python)
- Working with Social Graphs (Python)
- Recommending Movies at Scale (Python)
- Harvesting and Geolocating Twitter Data (Python)
- Optimizing Numerical Code w
8. Understanding MySQL Internals: Discovering and Improving a Great Database
Feature
Used Book in Good ConditionDescription
Although MySQL's source code is open in the sense of being publicly available, it's essentially closed to you if you don't understand it. In this book, Sasha Pachev -- a former member of the MySQL Development Team -- provides a comprehensive tour of MySQL 5 that shows you how to figure out the inner workings of this powerful database. You'll go right to heart of the database to learn how data structures and convenience functions operate, how to add new storage engines and configuration options, and much more.
The core of Understanding MySQL Internals begins with an Architecture Overview that provides a brief introduction of how the different components of MySQL work together. You then learn the steps for setting up a working compilable copy of the code that you can change and test at your pleasure. Other sections of the book cover:
- Core server classes, structures, and API
- The communication protocol between the client and the server
- Configuration variables, the controls of the server; includes a tutorial on how to add your own
- Thread-based request handling -- understanding threads and how they are used in MySQL
- An overview of MySQL storage engines
- The storage engine interface for integrating third-party storage engines
- The table lock manager
- The parser and optimizer for improving MySQL's performance
- Integrating a transactional storage engine into MySQL
- The internals of replication
Understanding MySQL Internals provides unprecedented opportunities for developers, DBAs, database application programmers, IT departments, software vendors, and computer science students to learn about the inner workings of this enterprise-proven database. With this book, you will soon reach a new level of comprehension regarding database development that will enable you to accomplish your goals. It's your guide to discovering and improving a great database.
9. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) by Kevin P. Murphy (2012-08-24)
10. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) by Kevin Murphy (2012-09-18)
Conclusion
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