Selected papers from the International Conference ML4CPS 2016
The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016.
Cyber Physical Systems more » are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. « less
Artificial Immune Systems and their Applications in Software Personalization
The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented more » as a valid metaphor towards the creation of abstract and high level representations of biological components or functions that lay the foundations for an alternative machine learning paradigm. Therefore, focus is given on addressing the primary problems of Pattern Recognition by developing Artificial Immune System-based machine learning algorithms for the problems of Clustering, Classification and One-Class Classification. Pattern Classification, in particular, is studied within the context of the Class Imbalance Problem. The main source of inspiration stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems that is exceptionally evolved in order to continuously address an extremely unbalanced pattern classification problem, namely, the self / non-self discrimination process. The experimental results presented in this monograph involve a wide range of degenerate binary classification problems where the minority class of interest is to be recognized against the vast volume of the majority class of negative patterns. In this context, Artificial Immune Systems are utilized for the development of personalized software as the core mechanism behind the implementation of Recommender Systems.
The book will be useful to researchers, practitioners and graduate students dealing with Pattern Recognition and Machine Learning and their applications in Personalized Software and Recommender Systems. It is intended for both the expert/researcher in these fields, as well as for the general reader in the field of Computational Intelligence and, more generally, Computer Science who wishes to learn more about the field of Intelligent Computing Systems and its applications. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her. « less
A Comprehensive Guide to Machine Learning
This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.
This new paradigm of teaching Machine Learning will bring about a radical change in perception more » for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.
For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.
All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.
Who This Book is For:
Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
What you will learn:
1. ML model building process flow2. Theoretical aspects of Machine Learning3. Industry based Case-Study4. Example based understanding of ML algorithm using R5. Building ML models using Apache Hadoop and Spark « less
A Test-Driven Approach
By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment.
You’ll learn how to dissect algorithms at a granular level, using various tests, and discover more » a framework for testing machine learning code. The author provides real-world examples to demonstrate the results of using machine-learning code effectively.
Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms. « less
* 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
* more » It is a practical and methodically explained guide that allows you to apply Tensorflow’s features from the very beginning.
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.
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
ABOUT THE AUTHOR
Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.
He has done research on high performance computing since 2005 and began studying and implementing convolutional neural networks in 2008,writing a CPU and GPU - supporting neural network feed forward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks, and is currently working on signal classification using ML techniques.
TABLE OF CONTENTS
1. Exploring and Transforming Data
3. Linear Regression
4. Logistic Regression
5. Simple FeedForward Neural Networks
6. Convolutional Neural Networks
7. Recurrent Neural Networks and LSTM
8. Deep Neural Networks
9. Running Models at Scale – GPU and Serving
10. Library Installation and Additional Tips « less
Design efficient machine learning systems that give you more accurate results
***** About This Book *****
* Gain an understanding of the machine learning design process
* Optimize machine learning systems for improved accuracy
* Understand common programming tools and techniques for machine learning
* Develop techniques and strategies for dealing with large amounts of data more » from a variety of sources
* Build models to solve unique tasks
***** Who This Book Is For *****
This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.
***** What You Will Learn *****
* Gain an understanding of the machine learning design process
* Optimize the error function of your machine learning system
* Understand the common programming patterns used in machine learning
* Discover optimizing techniques that will help you get the most from your data
* Find out how to design models uniquely suited to your task
***** In Detail *****
Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.
There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs.
This book will give you this understanding and more.
***** Style and approach *****
This easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective. « less
*** Key Features ***
* Design algorithms in F# to tackle complex computing problems
* Be a proficient F# data scientist using this simple-to-follow guide
* Solve real-world, data-related problems with robust statistical models, built for a range of datasets
*** Book Description ***
The F# more » functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.
If you want to learn how to use F# to build machine learning systems, then this is the book you want.
Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data.
*** What you will learn ***
* Use F# to find patterns through raw data
* Build a set of classification systems using Accord.NET, Weka, and F#
* Run machine learning jobs on the Cloud with MBrace
* Perform mathematical operations on matrices and vectors using Math.NET
* Use a recommender system for your own problem domain
* Identify tourist spots across the globe using inputs from the user with decision tree algorithms
*** About the Author ***
Sudipta Mukherjee was born in Kolkata and migrated to Bangalore. He is an electronics engineer by education and a computer engineer/scientist by profession and passion. He graduated in 2004 with a degree in electronics and communication engineering.
He has a keen interest in data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning at large. His first book on Data Structure using C has been received quite well. Parts of the book can be read on Google Books at http://goo.gl/pttSh. The book was also translated into simplified Chinese, available from Amazon.cn at http://goo.gl/lc536. This is Sudipta's second book with Packt Publishing. His first book, .NET 4.0 Generics (http://goo.gl/MN18ce), was also received very well. During the last few years, he has been hooked to the functional programming style. His book on functional programming, Thinking in LINQ (http://goo.gl/hm0lNF), was released last year. Last year, he also gave a talk at @FuConf based on his LINQ book (https://goo.gl/umdxIX). He lives in Bangalore with his wife and son.
Sudipta can be reached via e-mail at firstname.lastname@example.org and via Twitter at @samthecoder.
*** Table of Contents ***
1. Introduction to Machine Learning
2. Linear Regression
3. Classification Techniques
4. Information Retrieval
5. Collaborative Filtering
6. Sentiment Analysis
7. Anomaly Detection « less
An Illustrated Guide to Machine Learning
Artificial intelligence is changing our lives in ways we need to understand. Algorithms govern how we find information, how we learn, how we move, how we buy, what we buy, how we stay healthy, how we meet, whom we meet, how we are treated and what we are treated with. Marketing, analytics, diagnostics, more » manufacturing, driving, searching, speaking, seeing, hearing are all being disrupted and reshaped by machines that learn. Algorithms that can operate at the speed and scale that data is now generated are now making, what once was impossible, a practical reality.
The goal of this book is to get you up to speed on what drives the artificial intelligence you encounter today so you can understand what makes this field of computer science different from the software engineering of the past. It is aimed at executives who would like to use machine learning in their business and want to understand the underlying mechanics, and for anyone else who wants to understand more about the architectures driving artificial intelligence and machine learning. « less
Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of more » the subject.
You’ll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system. « less
A Guide for Data Scientists
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. more » 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 « less