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• Machine generated contents note: ch. 1 What Is Machine Learning?
• History of Machine Learning
• Alan Turing
• Arthur Samuel
• Tom M. Mitchell
• Summary Definition
• Algorithm Types for Machine Learning
• Supervised Learning
• Unsupervised Learning
• The Human Touch
• Uses for Machine Learning
• Software
• Spam Detection
• Voice Recognition
• Stock Trading
• Robotics
• Medicine and Healthcare
• Advertising
• Retail and E-commerce
• Gaming Analytics
• The Internet of Things
• Languages for Machine Learning
• Python
• R
• Matlab
• Scala
• Ruby
• Software Used in This Book
• Checking the Java Version
• Weka Toolkit
• DeepLearning4J
• Kafka
• Spark and Hadoop
• Text Editors and IDEs
• Data Repositories
• UC Irvine Machine Learning Repository
• Kaggle
• Summary
• ch. 2 Planning for Machine Learning
• The Machine Learning Cycle
• It All Starts with a Question
• I Don't Have Data!
• Starting Local
• Transfer Learning
• Competitions
• One Solution Fits All?
• Defining the Process
• Planning
• Developing
• Testing
• Reporting
• Refining
• Production
• Avoiding Bias
• Building a Data Team
• Mathematics and Statistics
• Programming
• Graphic Design
• Domain Knowledge
• Data Processing
• Using Your Computer
• A Cluster of Machines
• Cloud-Based Services
• Data Storage
• Physical Discs
• Cloud-Based Storage
• Data Privacy
• Cultural Norms
• Generational Expectations
• The Anonymity of User Data
• Don't Cross the "Creepy Line"
• Data Quality and Cleaning
• Presence Checks
• Type Checks
• Length Checks
• Range Checks
• Format Checks
• The Britney Dilemma
• What's in a Country Name?
• Dates and Times
• Final Thoughts on Data Cleaning
• Thinking About Input Data
• Raw Text
• Comma-Separated Variables
• JSON
• YAML
• XML
• Spreadsheets
• Databases
• Images
• Thinking About Output Data
• Don't Be Afraid to Experiment
• Summary
• ch. 3 Data Acquisition Techniques
• Scraping Data
• Copy and Paste
• Google Sheets
• Using an API
• Acquiring Weather Data
• Using the Command Line
• Using Java
• Using Clojure
• Migrating Data
• Installing Embulk
• Using the Quick Run
• Installing Plugins
• Migrating Files to Database
• Bulk Converting CSV to JSON
• Summary
• ch. 4 Statistics, Linear Regression, and Randomness
• Working with a Basic Dataset
• Loading and Converting the Dataset
• Loading Data with Clojure
• Loading Data with Java
• Introducing Basic Statistics
• Minimum and Maximum Values
• Mathematical Notation
• Clojure
• Java
• Sum
• Mathematical Notation
• Clojure
• Java
• Mean
• Arithmetic Mean
• Harmonic Mean
• Geometric Mean
• The Relationship Between the Three Averages
• Clojure
• Java
• Mode
• Clojure
• Java
• Median
• Clojure
• Java
• Range
• Clojure
• Java
• Interquartile Ranges
• Clojure
• Java
• Variance
• Clojure
• Java
• Standard Deviation
• Clojure
• Java
• Using Simple Linear Regression
• Using Your Spreadsheet
• Using Excel
• Loading the CSV Data
• Creating a Scatter Plot
• Showing the Trendline
• Showing the Equation and R2 Value
• Making a Prediction
• Writing a Program
• Embracing Randomness
• Finding Pi with Random Numbers
• Using Monte Carlo Pi in Clojure
• Is the Dart Within the Circle?
• Now Throw Lots of Darts!
• Summary
• ch. 5 Working with Decision Trees
• The Basics of Decision Trees
• Uses for Decision Trees
• Advantages of Decision Trees
• Limitations of Decision Trees
• Different Algorithm Types
• ID3
• C4.5
• CHAID
• MARS
• How Decision Trees Work
• Building a Decision Tree
• Manually Walking Through an Example
• Calculating Entropy
• Information Gain
• Rinse and Repeat
• Decision Trees in Weka
• The Requirement
• Training Data
• Relation
• Attributes
• Data
• Using Weka to Create a Decision Tree
• Creating Java Code from the Classification
• Testing the Classifier Code
• Thinking About Future Iterations
• Summary
• ch. 6 Clustering
• What Is Clustering?
• Where Is Clustering Used?
• The Internet
• Business and Retail
• Law Enforcement
• Computing
• Clustering Models
• How the K-Means Works
• Initialization
• Assignments
• Update
• Calculating the Number of Clusters in a Dataset
• The Rule of Thumb Method
• The Elbow Method
• The Cross-Validation Method
• The Silhouette Method
• K-Means Clustering with Weka
• Preparing the Data
• The Workbench Method
• Loading Data
• Clustering the Data
• Visualizing the Data
• The Command-Line Method
• Converting CSV File to ARFF
• The First Run
• Refining the Optimum Clusters
• Name That Cluster
• The Coded Method
• Create the Project
• The Cluster Code
• Printing the Cluster Information
• Making Predictions
• The Final Code Listing
• Running the Program
• Further Development
• Summary
• ch. 7 Association Rules Learning
• Where Is Association Rules Learning Used?
• Web Usage Mining
• Beer and Diapers
• How Association Rules Learning Works
• Support
• Confidence
• Lift
• Conviction
• Defining the Process
• Algorithms
• Apriori
• FP-Growth
• Mining the Baskets
• A Walk-Through
• The Raw Basket Data
• Using the Weka Application
• Inspecting the Results
• Summary
• ch. 8 Support Vector Machines
• What Is a Support Vector Machine?
• Where Are Support Vector Machines Used?
• The Basic Classification Principles
• Binary and Multiclass Classification
• Linear Classifiers
• Confidence
• Maximizing and Minimizing to Find the Line
• How Support Vector Machines Approach Classification
• Using Linear Classification
• Using Non-Linear Classification
• Using Support Vector Machines in Weka
• Installing LibSVM
• Weka LibSVM Installation
• A Classification Walk-Through
• Setting the Options
• Running the Classifier
• Dealing with Errors from LibSVM
• Saving the Model
• Implementing LibSVM with Java
• Converting csv Data to .arff Format
• Setting Up the Project and Libraries
• Training and Predicting with the Existing Data
• Summary
• ch. 9 Artificial Neural Networks
• What Is a Neural Network?
• Artificial Neural Network Uses
• High-Frequency Trading
• Credit Applications
• Data Center Management
• Robotics
• Medical Monitoring
• Trusting the Black Box
• Breaking Down the Artificial Neural Network
• Perceptrons
• Activation Functions
• Multilayer Perceptrons
• Back Propagation
• Data Preparation for Artificial Neural Networks
• Artificial Neural Networks with Weka
• Generating a Dataset
• Loading the Data into Weka
• Configuring the Multilayer Perceptron
• Learning Rate
• Hidden Layers
• Training Time
• Training the Network
• Altering the Network
• Which Bit Is Which?
• Adding Nodes
• Connecting Nodes
• Removing Connections
• Removing Nodes
• Increasing the Test Data Size
• Implementing a Neural Network in Java
• Creating the Project
• Writing the Code
• Converting from CSV to Arff
• Running the Neural Network
• Developing Neural Networks with DeepLearning4J
• Modifying the Data
• Viewing Maven Dependencies
• Handling the Training Data
• Normalizing Data
• Building the Model
• Evaluating the Model
• Saving the Model
• Building and Executing the Program
• Summary
• ch. 10 Machine Learning with Text Documents
• Preparing Text for Analysis
• Apache Tika
• Downloading Tika
• Tika from the Command Line
• Tika Within an Application
• Cleaning the Text Data
• Convert Words to Lowercase
• Remove Punctuation
• Stopwords
• Stemming
• N-grams
• TF/IDF
• Loading the Documents
• Calculating the Term Frequency
• Calculating the Inverse Document Frequency
• Computing the TF/IDF Score
• Reviewing the Final Code Listing
• Word2Vec
• Loading the Raw Text Data
• Tokenizing the Strings
• Creating the Model
• Evaluating the Model
• Reviewing the Final Code
• Basic Sentiment Analysis
• Loading Positive and Negative Words
• Loading Sentences
• Calculating the Sentiment Score
• Reviewing the Final Code
• Performing a Test Run
• Further Development
• Summary
• ch. 11 Machine Learning with Images
• What Is an Image?
• Introducing Color Depth
• Images in Machine Learning
• Basic Classification with Neural Networks
• Basic Settings
• Loading the MNIST Images
• Model Configuration
• Model Training
• Model Evaluation
• Convolutional Neural Networks
• How CNNs Work
• Feature Extraction
• Activation Functions
• Pooling
• Classification
• CNN Demonstration
• Downloading the Image Data
• Basic Setup
• Handling the Training and Test Data
• Image Preparation
• CNN Model Configuration
• Model Training
• Model Evaluation
• Saving the Model
• Transfer Learning
• Summary
• ch. 12 Machine Learning Streaming with Kafka
• What You Will Learn in This Chapter
• From Machine Learning to Machine Learning Engineer
• From Batch Processing to Streaming Data Processing
• What Is Kafka?
• How Does It Work?
• Fault Tolerance
• Further Reading
• Installing Kafka
• Kafka as a Single-Node Cluster
• Starting Zookeeper
• Starting Kafka
• Kafka as a Multinode Cluster
• Starting the Multibroker Cluster
• Topics Management
• Creating Topics
• Finding Out Information About Existing Topics
• Deleting Topics
• Sending Messages from the Command Line
• Receiving Messages from the Command Line
• Kafka Tool UI
• Writing Your Own Producers and Consumers
• Producers in Java
• Properties
• The Producer
• Messages
• The Final Code
• Message Acknowledgments
• Consumers in Java
• Properties
• Windows
• Linux
• Your First Run
• Installing R-Studio
• The R Basics
• Variables and Vectors
• Matrices
• Lists
• Data Frames
• Installing Packages
• Loading in Data
• CSV Files
• MySQL Queries
• Creating Random Sample Data
• Plotting Data
• Bar Charts
• Pie Charts
• Dot Plots
• Line Charts
• Simple Statistics
• Simple Linear Regression
• Creating the Data
• The Initial Graph
• Regression with the Linear Model
• Making a Prediction

• Note continued: Basic Sentiment Analysis
• Using Functions to Load in Word Lists
• Writing a Function to Score Sentiment
• Testing the Function
• Apriori Association Rules
• Installing the arules Package
• Gathering the Training Data
• Importing the Transaction Data
• Running the Apriori Algorithm
• Inspecting the Results
• Accessing R from Java
• Installing the rJava Package
• Creating Your First Java Code in R
• Calling R from Java Programs
• Setting Up an Eclipse Project
• Creating the Java/R Class
• Running the Example
• Extending Your R Implementations
• Connecting to Social Media with R
• Summary
• Appendix A Kafka Quick Start
• Installing Kafka
• Starting Zookeeper
• Starting Kafka
• Creating Topics
• Listing Topics
• Describing a Topic
• Deleting Topics
• Running a Console Producer
• Running a Console Consumer
• Appendix B The Twitter API Developer Application Configuration
• Appendix C Useful Unix Commands
• Using Sample Data
• Showing the Contents: cat, more, and less
• Example Command
• Expected Output
• Filtering Content: grep
• Example Command for Finding Text
• Example Output
• Sorting Data: sort
• Example Command for Basic Sorting
• Example Output
• Finding Unique Occurrences: uniq
• Showing the Top of a File: head
• Counting Words: wc
• Locating Anything: find
• Combining Commands and Redirecting Output
• Picking a Text Editor
• Colon Frenzy: Vi and Vim
• Nano
• Emacs
• Appendix D Further Reading
• Machine Learning
• Statistics
• Big Data and Data Science
• Visualization
• Making Decisions
• Datasets
• Blogs
• Useful Websites
• The Tools of the Trade.

Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: * Learn the languages of machine learning including Hadoop, Mahout, and Weka * Understand decision trees, Bayesian networks, and artificial neural networks * Implement Association Rule, Real Time, and Batch learning * Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
(source: Nielsen Book Data)

• Getting Started with Excel 2013 Working with data and Excel tables Performing calculations on data Changing workbook appearance Focusing on specific data by using filters Reordering and summarizing data Combining data from multiple sources Analyzing data and alternative data sets Creating charts and graphics Using PivotTables and PivotCharts Printing worksheets and charts Working with macros and forms Working with other Office programs.
• (source: Nielsen Book Data)

The smart way to learn Excel 2013-one step at a time! Experience learning made easy-and quickly teach yourself how to manage, analyze, and present data with Excel 2013. With Step by Step, you set the pace-building and practicing the skills you need, just when you them! Includes downloadable practice files and companion eBook. Discover how to: Work with Excel 2013 in touch mode Write formulas, create macros, and organize data Present data visually and add images to worksheets Consolidate multiple sets of data into a single workbook Analyze data using the Quick Analysis Lens Collaborate with colleagues and present workbooks online.
(source: Nielsen Book Data)

• Modifying a program
• Calculating
• Double, double
• Programming fundamentals
• Standard starting points
• Text I
• Text II
• Image I
• Image II
• Text III
• Statistics and visualization
• Animation
• Sound
• Interaction
• Onward
• Appendix A: Why program?
• Appendix B: Contexts for learning

"Introduces programming to readers with a background in the arts and humanities; there are no prerequisites, and no knowledge of computation is assumed . The book uses Python and Processing, both of which are free software, as the primary programming languages"--Back cover

• What is machine learning?
• Planning machine learning
• Working with decision trees
• Bayesian networks
• Artificial neural networks
• Association rules learning
• Support vector machines
• Clustering
• Machine learning in real time with Spring XD
• Maching learning as a batch process
• Apache Spark
• Machine learning with R.

Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: * Learn the languages of machine learning including Hadoop, Mahout, and Weka * Understand decision trees, Bayesian networks, and artificial neural networks * Implement Association Rule, Real Time, and Batch learning * Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
(source: Nielsen Book Data)

• Chapter 1 Working with Excel Getting to Know Excel 4 Start Excel 6 Tour the Excel Window 8 Work with the Excel Ribbon 9 Work with the Excel Galleries 10 Customize the Quick Access Toolbar 12 Customize the Ribbon 14 Change the View 16 Configure Excel Options 18 Add Excel to the Windows Taskbar 20 Quit Excel 21
• Chapter 2 Entering Data Learning the Layout of a Worksheet 24 Understanding the Types of Data You Can Use 25 Enter Text into a Cell 26 Enter a Number into a Cell 28 Enter a Date or Time into a Cell 30 Insert a Symbol 32 Edit Cell Data 34 Delete Data from a Cell 36
• Chapter 3 Working with Ranges Select a Range 40 Fill a Range with the Same Data 42 Fill a Range with a Series of Values 44 Flash Fill a Range 46 Move or Copy a Range 48 Insert a Row or Column 50 Insert a Cell or Range 52 Delete Data from a Range 54 Delete a Range 56 Hide a Row or Column 58 Freeze Rows or Columns 60 Merge Two or More Cells 62 Transpose Rows and Columns 64
• Chapter 4 Working with Range Names Understanding the Benefits of Using Range Names 68 Define a Range Name 70 Use Worksheet Text to Define a Range Name 72 Navigate a Workbook Using Range Names 74 Change a Range Name 76 Delete a Range Name 78
• Chapter 5 Formatting Excel Ranges Change the Font and Font Size 82 Apply Font Effects 84 Change the Font Color 86 Align Text Within a Cell 88 Center Text Across Multiple Columns 90 Rotate Text Within a Cell 92 Add a Background Color to a Range 94 Apply a Number Format 96 Change the Number of Decimal Places Displayed 98 Apply an AutoFormat to a Range 100 Apply a Conditional Format to a Range 102 Apply a Style to a Range 104 Change the Column Width 106 Change the Row Height 108 Wrap Text Within a Cell 110 Add Borders to a Range 112
• Chapter 6 Building Formulas Understanding Excel Formulas 116 Build a Formula 118 Understanding Excel Functions 120 Add a Function to a Formula 122 Add a Row or Column of Numbers 124 Build an AutoSum Formula 126 Add a Range Name to a Formula 128 Reference Another Worksheet Range in a Formula 130 Move or Copy a Formula 132
• Chapter 7 Manipulating Worksheets Navigate a Worksheet 136 Rename a Worksheet 137 Create a New Worksheet 138 Move a Worksheet 140 Copy a Worksheet 142 Delete a Worksheet 144 Change the Gridline Color 146 Toggle Worksheet Gridlines On and Off 148 Toggle Worksheet Headings On and Off 149
• Chapter 8 Dealing with Workbooks Create a New Blank Workbook 152 Create a New Workbook from a Template 154 Save a Workbook 156 Open a Workbook 157 Arrange Workbook Windows 158 Find Text in a Workbook 160 Replace Text in a Workbook 162
• Chapter 9 Formatting Workbooks Modify the Workbook Colors 166 Set the Workbook Fonts 168 Choose Workbook Effects 170 Apply a Workbook Theme 172 Add a Workbook Header 174 Add a Workbook Footer 176
• Chapter 10 Working with Tables Understanding Tables 180 Get to Know Table Features 181 Convert a Range to a Table 182 Select Table Data 184 Insert a Table Row 186 Insert a Table Column 187 Delete a Table Row 188 Delete a Table Column 189 Add a Column Subtotal 190 Convert a Table to a Range 192 Apply a Table Style 193 Build a Custom Table Style 194
• Chapter 11 Analyzing Data Sort a Range or Table 198 Filter a Range or Table 200 Set Data Validation Rules 202 Create a Data Table 204 Summarize Data with Subtotals 206 Group Related Data 208 Analyze Data with Goal Seek 210 Analyze Data with Scenarios 212 Remove Duplicate Values from a Range or Table 216
• Chapter 12 Visualizing Data with Charts Examine Chart Elements 220 Understanding Chart Types 221 Create a Chart 222 Create a Recommended Chart 224 Add Chart Titles 226 Add Data Labels 227 Position the Chart Legend 228 Display Chart Gridlines 229 Display a Data Table 230 Change the Chart Layout and Style 231 Select a Different Chart Type 232 Change the Chart Source Data 234 Move or Resize a Chart 236
• Chapter 13 Collaborating with Others Add a Comment to a Cell 240 Protect a Worksheet s Data 242 Protect a Workbook s Structure 244 Share a Workbook with Other Users 246 Track Workbook Changes 248 Accept or Reject Workbook Changes 250 Save a Workbook to Your SkyDrive 252 Send a Workbook as an E-Mail Attachment 254 Save Excel Data as a Web Page 256 Make a Workbook Compatible with Earlier Versions of Excel 258 Mark Up a Worksheet with a Digital Pen 260 Collaborate on a Workbook Online 262.
• (source: Nielsen Book Data)

A friendly, visual approach to learning the basics of Excel 2013 As the world's leading spreadsheet program, Excel is a spreadsheet and data analysis tool that is part of the Microsoft Office suite. The new Excel 2013 includes new features and functionalities that require users of older versions to re-learn the application. However, whether you're switching from an earlier version or learning Excel for the first time, this easy-to-follow visual guide gets you going with Excel 2013 quickly and easily. Numbered steps as well as full-color screen shots, concise information, and helpful tips, all contribute to a clear, comfortable learning experience. * Covers Excel basics, workbook fundamentals, formatting worksheets and enhancing them with graphics, analyzing data, communicating results with charts and PivotTables, and much more * Walks you through using Flash Fill, representing your data in a variety of ways, and maximizing a more interactive interface * Demonstrates how to share workbooks, collaborate with others, and embed portions of your spreadsheets on social network pages Excel 2013 Simplified is a quick and easy way to get up to speed on Excel 2013 for both novice users and those upgrading from an earlier version.
(source: Nielsen Book Data)

The smart way to learn Excel 2013-one step at a time! Experience learning made easy-and quickly teach yourself how to manage, analyze, and present data with Excel 2013. With Step by Step, you set the pace-building and practicing the skills you need, just when you them! Includes downloadable practice files and companion eBook. Discover how to: Work with Excel 2013 in touch mode Write formulas, create macros, and organize data Present data visually and add images to worksheets Consolidate multiple sets of data into a single workbook Analyze data using the Quick Analysis Lens Collaborate with colleagues and present workbooks online.
(source: Nielsen Book Data)

• 1. The ideas men.
• 2. Ivory towers and tea rooms.
• 3. The Manchester machines
• 4. Meanwhile, in deepest Hertfordshire
• 5. One man in a barn
• 6. Aces and Deuces
• 7. Into the market place
• 8. Hindsight and foresight: Turing's legacy
• 9. Bibliography and references
• 10. Timeline: Alan Turing's life with computers.
• 11. Index.
• (source: Nielsen Book Data)

Secret wartime projects in code-breaking, radar and ballistics produced a wealth of ideas and technologies that kick-started the development of digital computers. This is the story of the people and projects that flourished in the post-war period. By 1955 computers had begun to appear in the market-place. The Information Age was dawning and Alan Turing and his contemporaries held centre stage. Their influence is still discernable deep down within today's hardware and software.
(source: Nielsen Book Data)

• Introduction
• Applied math and machine learning basics. Linear algebra
• Probability and information theory
• Numerical computation
• Machine learning basics
• Deep networks: modern practices. Deep feedforward networks
• Regularization for deep learning
• Optimization for training deep models
• Convolutional networks
• Sequence modeling: recurrent and recursive nets
• Practical methodology
• Applications
• Deep learning research. Linear factor models
• Autoencoders
• Representation learning
• Structured probabilistic models for deep learning
• Monte Carlo methods
• Confronting the partition function
• Approximate inference
• Deep generative models

"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 video games. 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"--Page 4 of cover

• Acknowledgments Foreword Introduction Chapter 1 We Perceive What We Expect Chapter 2 Our Vision is Optimized to See Structure Chapter 3 We Seek and Use Visual Structure Chapter 4 Reading is Unnatural Chapter 5 Our Color Vision is Limited Chapter 6 Our Peripheral Vision is Poor Chapter 7 Our Attention is Limited
• Our Memory is Imperfect Chapter 8 Limits on Attention, Shape, Thought and Action Chapter 9 Recognition are Easy
• Recall is Hard Chapter 10 Learning from Experience and Performing Learned Actions are Easy
• Problem Solving and Calculation are Hard Chapter 11 Many Factors Affect Learning Chapter 12 We Have Time Requirements Epilogue Appendix Bibliography Index.
• (source: Nielsen Book Data)

Early user interface (UI) practitioners were trained in cognitive psychology, from which UI design rules were based. But as the field evolves, designers enter the field from many disciplines. Practitioners today have enough experience in UI design that they have been exposed to design rules, but it is essential that they understand the psychology behind the rules in order to effectively apply them. In Designing with the Mind in Mind, Jeff Johnson, author of the best selling GUI Bloopers, provides designers with just enough background in perceptual and cognitive psychology that UI design guidelines make intuitive sense rather than being just a list of rules to follow.
(source: Nielsen Book Data)

• Chapter 1: About this book
• Chapter 2: Creating a custom Web App
• Chapter 3: Modifying a Web App
• Chapter 4: Creating a desktop database
• Chapter 5: Working with data in datasheets
• Chapter 6: Selecting data using queries
• Chapter 7: Modifying data using queries
• Chapter 8: Improving presentations with forms
• Chapter 9: Using controls effectively
• Chapter 10: Preparing data to print using reports
• Chapter 11: Exchanging data
• Chapter 12: Introducing the power of macros
• Chapter 13: Administrating a database About the Author.
• (source: Nielsen Book Data)

Learn the simplest ways to get things done with Microsoft Access 2013 Get the full-color, visual guide that makes learning Microsoft Access 2013 plain and simple! Follow the book's easy steps and screenshots and clear, concise language to learn the simplest ways to build a database and sort information. Here's WHAT you'll learn: Build and manage your own database Apply professional designs across your databases Create presentations and publish reports online Secure your database with passwords and encryption Exchange data with other databases and documents Interact with your data easier using macros Here's HOW you'll learn it: Jump in wherever you need answers Follow easy STEPS and SCREENSHOTS to see exactly what to do Get handy TIPS for new techniques and shortcuts Use TRY THIS! Exercises to apply what you learn right away.
(source: Nielsen Book Data)

• Invitation to a Journey in the ERA of Cloud Computing / Dana Petcu
• Part I: Cloud Services
• 1. Open Computing Infrastructures for Elastic Services: Contrail Cloud Federation / Piyush Harsh, Yvon Jégou, Roberto G. Cascella and Christine Morin
• 2. Towards Autonomic Transactional Replication for Cloud Environments / Maria Couceiro, Paolo Romano and Luís Rodrigues
• 3. Data-intensive Storage Services on Clouds: Limitations, Challenges and Enablers / Elliot K. Kolodner, Alexandra Shulman-Peleg, Dalit Naor, Per Brand, Michel Dao, Albert Eckert, Spyridon V. Gogouvitis, Danny Harnik, Michael C. Jaeger, Dimosthenis P. Kyriazis, Mirko Lorenz, Alberto Messina, Aidan Shribman, Sivan Tal, Athanasios S. Voulodimos and Yaron Wolfsthal
• 4. Migrating Legacy Applications to the Service Cloud Paradigm: The REMICS Project / Parastoo Mohagheghi, Franck Barbier, Arne J. Berre, Brice Morin, Andrey Sadovykh, Thor Sæther, Alexis Henry, Antonin Abhervé, Tom Ritter, Christian Hein and Michał Śmiałek
• Part II: Cloud Management
• 5. Towards Holistic Cloud Management / Johan Tordsson, Karim Djemame, Daniel Espling, Gregory Katsaros, Wolfgang Ziegler, Oliver Wäldrich, Kleopatra Konstanteli, Ali Sajjad, Muttukrishnan Rajarajan, Georgina Gallizo and Srijith Nair
• 6. TClouds: Privacy and Resilience for Internet-scale Critical Infrastructures / Alysson Bessani, Imad M. Abbadi, Sven Bugiel, Emanuele Cesena, Mina Deng, Michael Gröne, Ninja Marnau, Stefan Nürnberger, Marcelo Pasin and Norbert Schirmer
• Part III: Cloud Automation
• 7. Agent Based Services for Negotiation, Monitoring and Reconfiguration of Cloud Resources / Salvatore Venticinque
• 8. Design for Self-adaptation in Service-oriented Systems in the Cloud / Antonio Bucchiarone, Cinzia Cappiello, Elisabetta Di Nitto, Sergei Gorlatch, Dominique Meiländer and Andreas Metzger
• 9. Service Level Management and Service Delivery Management: Challenges in Federated e-Infrastructures / Matti Heikkurinen and Owen Appleton
• Part IV: Cloud Adoption
• 10. StratusLab Cloud Distribution / Charles Loomis, Mohammed Airaj, Marc-Elian Bégin, Evangelos Floros, Stuart Kenny and David O'Callaghan
• 11. Business Application Governance and SLA Management in PaaS Context / Francesco D'Andria, Ilknur Chulani and Philipp Strube
• 12. Reducing Time to Market with the Platform as a Service Cloud of the Future / José Luis Vázquez-Poletti, Eduardo Oliveros, Rafael Moreno-Vozmediano, Ignacio M. Llorente, Sebastian Ortega, Miguel Jimenez, Javier Soriano and Andreas Menychtas.

What's new in the European research and development area? Cloud computing is a provision model where whatever computing resource that can be thought of (machines, network, software solutions, applications) is provided as a service. This new paradigm has changed the center of gravity of computing in both the academic and industry environments, but despite the considerable efforts and investments, there are critical problems that are not yet solved. The research and development community involved in distributed computing is searching for viable solutions that will increase the adoption of the cloud. This is the case of the collaborative work done by multi-national teams in the context of the FP7 programme of the European Commission. Students, researchers and developers working in the field of distributed computing will find in this book a snapshot of the on-going activities in research and development of cloud computing undertaken at the European level. These activities are organized by the latest hot topics of cloud computing research, which include services, management, automation and adoption. Summarizing, this book will help the reader understand and identify the research and development winds that are pushing the clouds to Europe.
(source: Nielsen Book Data)

• Part I: Bridging Flash and After Effects: Getting Started in After Effects
• From Flash to After Effects
• From After Effects to Flash
• Part II: Enhancing Flash Through After Effects: Alpha Channels
• Motion Graphics
• The Third Dimension
• Character Animation
• Visual Effects
• Shapes and Sounds
• Expressions and ActionScript
• Part III: Publishing: Optimization and Rendering.
• (source: Nielsen Book Data)

Flash Designers: push Flash to the next level with After Effects' robust toolset. CS5 delivers more complete integration of these two powerhouse applications-so you can expand your multimedia horizons. Flash + After Effects gives you a working understanding of the AE toolset and professional techniques that raise the design bar for web, HD broadcast, or CD/DVD delivery. The companion web site contains project media for hands-on practice of essential production skills, including: * Working with live video in Flash and the After Effects timeline * Using alpha channels for keying and enhancing interactivity * Integrating new CS5 features including the Roto Brush and Code Snippets * Exporting layered After Effects compositions for Flash * Building interactive 3D environments with video, cameras, and lights * Enhancing character animation using IK and Puppet Tools * Exporting video in HD and for the Web.
(source: Nielsen Book Data)
Flash Designers: push Flash to the next level with After Effects' robust toolset. CS5 delivers more complete integration of these two powerhouse applications-so you can expand your multimedia horizons. Flash + After Effects gives you a working understanding of the AE toolset and professional techniques that raise the design bar for web, HD broadcast, or CD/DVD delivery. The companion web site contains project media for hands-on practice of essential production skills, including:.
(source: Nielsen Book Data)

• chapter 1 User Experience and Why It Matters
• chapter 2 Meet the Elements
• chapter 3 The Strategy Plane Product Objectives and User Needs
• chapter 4 The Scope Plane Functional Specifications and Content Requirements
• chapter 5 The Structure Plane Interaction Design and Information Architecture
• chapter 6 The Skeleton Plane Interface Design, Navigation Design, and Information Design
• chapter 7 The Surface Plane Sensory Design
• chapter 8 The Elements Applied Index.
• (source: Nielsen Book Data)

From the moment it was published almost ten years ago, Elements of User Experience became a vital reference for web and interaction designers the world over, and has come to define the core principles of the practice. Now, in this updated, expanded, and full-color new edition, Jesse James Garrett has refined his thinking about the Web, going beyond the desktop to include information that also applies to the sudden proliferation of mobile devices and applications. Successful interaction design requires more than just creating clean code and sharp graphics. You must also fulfill your strategic objectives while meeting the needs of your users. Even the best content and the most sophisticated technology won't help you balance those goals without a cohesive, consistent user experience to support it. With so many issues involved-usability, brand identity, information architecture, interaction design- creating the user experience can be overwhelmingly complex. This new edition of The Elements of User Experience cuts through that complexity with clear explanations and vivid illustrations that focus on ideas rather than tools or techniques. Garrett gives readers the big picture of user experience development, from strategy and requirements to information architecture and visual design.
(source: Nielsen Book Data)

• Part I. Setting the scene
• part II. Functional dependencies, Boyce/Codd normal form, and related matters
• part III. Join dependencies, fifth normal form, and related matters
• part IV. Orthogonality
• part V. Redundancy.

Because databases often stay in production for decades, careful design is critical to making the database serve the needs of your users over years, and to avoid subtle errors or performance problems. In this book, CJ Date, a leading exponent of relational databases, lays out the principles of good database design. Database Design and Relational Theory investigates denormalization, implicit dependencies, redundancy, and many other topics. DBAs will produce better designs and keep their data centers running better through the ideas conveyed in this book. Questions that this book answers include: What is Heath's Theorem, and why is it important? What is The Principle of Orthogonal Design? What makes some JDs reducible and others irreducible? What is dependency preservation, and why is it important? Should data redundancy always be avoided? Can it be?
(source: Nielsen Book Data)

• Part I: Cloud Base Tools and Technologies for Building Clouds A Taxonomy, Survey and Issues of Cloud Computing Ecosystems Towards a Taxonomy for Cloud Computing from an e-Science Perspective Examining Cloud Computing from the Perspective of Grid and Computer-Supported Cooperative Work Overview of Cloud Standards Part II: Cloud Seeding Open and Interoperable Clouds: the Cloud@HomeWay A Peer-to-Peer Framework for Supporting MapReduce Applications in Dynamic Cloud Environments Enhanced Network Support for Scalable Computing Clouds YML-PC: A Reference Architecture Based on Workflow for Building Scientific Private Clouds An Efficient Framework for Running Applications on Clusters, Grids and Clouds Resource Management for Hybrid Grid and Cloud Computing Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing Mixing Grids and Clouds: High-throughput Science using the Nimrod Tool Family Part III: Cloud Breaks Cloud Compliance: A Framework for Using Cloud Computing in a Regulated World Cloud Computing: Data Confidentiality and Interoperability Challenges Security Issues to Cloud Computing Securing the Cloud Part IV: Cloud Feedback Technologies for Enforcement and Distribution of Policy in Cloud Architectures The PRISM On-demand Digital Media Cloud Cloud Economics: Principles, Costs and Benefits Towards Application-Specific Service Level Agreements: Experiments in Clouds and Grids.
• (source: Nielsen Book Data)

Cloud computing continues to emerge as a subject of substantial industrial and academic interest. Although the meaning and scope of "cloud computing" continues to be debated, the current notion of clouds blurs the distinctions between grid services, web services, and data centers, among other areas. Clouds also bring considerations of lowering the cost for relatively bursty applications to the fore. Cloud Computing: Principles, Systems and Applications is an essential reference/guide that provides thorough and timely examination of the services, interfaces and types of applications that can be executed on cloud-based systems. The book identifies and highlights state-of-the-art techniques and methods for designing cloud systems, presents mechanisms and schemes for linking clouds to economic activities, and offers balanced coverage of all related technologies that collectively contribute towards the realization of cloud computing. With an emphasis on the conceptual and systemic links between cloud computing and other distributed computing approaches, this text also addresses the practical importance of efficiency, scalability, robustness and security as the four cornerstones of quality of service. Topics and features: explores the relationship of cloud computing to other distributed computing paradigms, namely peer-to-peer, grids, high performance computing and web services; presents the principles, techniques, protocols and algorithms that can be adapted from other distributed computing paradigms to the development of successful clouds; includes a Foreword by Professor Mark Baker of the University of Reading, UK; examines current cloud-practical applications and highlights early deployment experiences; elaborates the economic schemes needed for clouds to become viable business models. This book will serve as a comprehensive reference for researchers and students engaged in cloud computing. Professional system architects, technical managers, and IT consultants will also find this unique text a practical guide to the application and delivery of commercial cloud services. Prof. Nick Antonopoulos is Head of the School of Computing, University of Derby, UK. Dr. Lee Gillam is a Lecturer in the Department of Computing at the University of Surrey, UK.
(source: Nielsen Book Data)

• Getting Started
• The Node Module System
• The Node Programming Model
• Events and Timers
• The Command Line Interface
• The File System
• Streams
• Binary Data
• Executing Code
• Network Programming
• HTTP
• The Express Framework
• The Real-Time Web
• Databases
• Logging, Debugging, and Testing
• Application Scaling
• JavaScript Object Notation.

Since it's creation in 2009, Node.js has grown into a powerful and increasingly popular asynchronous-development framework for creating highly-scalable network applications using JavaScript. Respected companies such as Dow Jones and LinkedIn are among the many organizations to have seen Node's potential and adopted it into their businesses. Pro Node.js for Developers provides a comprehensive guide to this exciting new technology. We introduce you to Node -- what it is, why it matters and how to set it up -- before diving deeply into the key concepts and APIs that underpin its operation. Building upon your existing JavaScript skills you'll be shown how to use Node.js to build both Web- and Network-based applications, to deal with data sources, capture events and deal with child processes to create robust applications that will work well in a wide range of circumstances. Once you've mastered these skills we'll go further, teaching you more advanced software engineering skills that will give your code a professional edge. You'll learn how to create easily reusable modules that will save you time through code reuse, to log and debug your applications quickly and effectively and to write code that will scale easily and reliably as the demand for your application grows. What you'll learn * Install, configure and deploy Node.js apps effectively * Understand the Node.js asynchronous programming model in detail * Create both web and network-based Node.js applications with ease * Learn to work effectively with varied data sources and file types * Discover advanced software engineering concepts that will will save you time and promote code reuse Who this book is for This book is for developers who already have a working knowledge of JavaScript and are interested in adopting Node.js into their projects. All other concepts will be introduced from first-principals.
(source: Nielsen Book Data)

• Introduction 1 Part I: Getting Started with Access 2013 9
• Chapter 1: Access 2013 Basic Training 11
• Chapter 2: Navigating the Access Workspace 33
• Chapter 3: Database Basics 55
• Part II: Setting the Table 73
• Chapter 4: Table Tune Ups 75
• Chapter 5: Remodeling Your Data 91
• Chapter 6: Types, Masks, and Triggers 109
• Part III: Data Management Mania 131
• Chapter 7: A Form for All Reasons 133
• Chapter 8: Importing and Exporting Data 147
• Chapter 9: Automatic Data Editing 159
• Chapter 10: Access and the Web 171
• Part IV: Ask Your Data, and Ye Shall Receive Answers 193
• Chapter 11: Finding, Filtering, and Sorting Your Data - Fast 195
• Chapter 12: I Was Just Asking for Answers 211
• Chapter 13: I Want These AND Those OR Them 237
• Chapter 14: Number Crunching with the Total Row 245
• Chapter 15: Express Yourself with Formulas 257
• Chapter 16: Take Charge with Action Queries 271
• Part V: Simple and Snazzy Reporting 281
• Chapter 17: Fast and Furious Automatic Reporting 283
• Chapter 18: Professionally Designed Reports Made Easy 305
• Chapter 19: Headers and Footers and Groups, Oh My! 331
• Chapter 20: Magical Mass Mailings 353
• Part VI: More Power to You 361
• Chapter 21: Analyze This! 363
• Chapter 22: Steer Users in the Right Direction with Navigation Forms 375
• Part VII: The Part of Tens 383
• Chapter 23: Ten Common Problems 385
• Chapter 24: Ten Uncommon Tips 397
• Appendix: Getting Help 405
• Index 413.
• (source: Nielsen Book Data)

The easy guide to Microsoft Access returns with updates on the latest version! Microsoft Access allows you to store, organize, view, analyze, and share data; the new Access 2013 release enables you to build even more powerful, custom database solutions that integrate with the web and enterprise data sources. Access 2013 For Dummies covers all the new features of the latest version of Accessand serves as an ideal reference, combining the latest Access features with the basics of building usable databases. You'll learn how to create an app from the Welcome screen, get support for your desktop databases, and much more. Includes coverage of all the new features of Access 2013, including the updated interface Shows you how to create and share reports Features special videos and materials created by the authors to help reinforce the lessons included in the book Helps you build data analysis and interface tools for your specific needs Offers plenty of techniques and tips for solving common problems Access 2013 For Dummies provides you with access to the latest version of this database tool.
(source: Nielsen Book Data)

• Introduction 1 Part I: Getting Started with PowerPoint 2013 7
• Chapter 1: Welcome to PowerPoint 2013 9
• Chapter 2: Editing Slides 33
• Chapter 3: Working in Outline View 49
• Chapter 4: Proofi ng Your Presentations 55
• Chapter 5: Don t Forget Your Notes! 65
• Chapter 6: Show Time! 71 Part II: Creating Great-Looking Slides 87
• Chapter 7: All about Fonts and Text Formatting 89
• Chapter 8: Designing Your Slides 105
• Chapter 9: Animating Your Slides 119
• Chapter 10: Masters of the Universe Meet the Templates of Doom 135 Part III: Embellishing Your Slides 155
• Chapter 11: Inserting Pictures 157
• Chapter 12: Drawing on Your Slides 175
• Chapter 13: Charting for Fun and Profit 193
• Chapter 14: Working with SmartArt 211
• Chapter 15: Lights! Camera! Action! (Adding Sound and Video) 223
• Chapter 16: More Things to Insert on Your Slides 235 Part IV: Working with Others 261
• Chapter 17: Collaborating in the Cloud 263
• Chapter 18: Using a Slide Library and Other Ways to Reuse Slides 275
• Chapter 19: Exporting Your Presentation to Other Formats 281 Part V: The Part of Tens 293
• Chapter 20: Ten PowerPoint Commandments 295
• Chapter 21: Ten Tips for Creating Readable Slides 299
• Chapter 22: Ten Tips for Creating Readable Slides 303 Index 307.
• (source: Nielsen Book Data)

Get up and running with this full-color guide to PowerPoint 2013! PowerPoint, the number one presentation software, has been revised and improved with the introduction of Microsoft Office 2013. With this all-new, full-color book by your side, you will learn how to take full advantage of all of PowerPoint's powerful and dynamic capabilities. Bestselling veteran For Dummies, author Doug Lowe breaks it all down so that you can create a powerful and effective slideshow presentation with the new wide-screen theme and variant that incorporates videos, pictures, and shapes, and allows you to create customized icons using powerful tools. * Shows you how to create presentations with pizzazz using the new collection of themes, and then helps you align shapes, text boxes, and graphics * Zeroes in on all of PowerPoint's updated features, such as zooming in and out smoothly, switch slides easily (in or out of sequence), and projecting to a second screen * Explains how to Place and track comments next to the text you're discussing so everyone can see who replied to whom, and when * Highlights ways to work with hyperlinks, create web pages with PowerPoint, video edit, and much more PowerPoint 2013 For Dummies points you to the power of this updated application so that you can create effective and impressive presentations.
(source: Nielsen Book Data)

• Introduction Taking a Microsoft Office Specialist exam Exam 77-422 Microsoft PowerPoint 2013
• Chapter 1: Create and Manage Presentations Chapter 2: Insert and Format Slides and Shapes Chapter 3: Create Slide Content Chapter 4: Apply Transitions and Animations Chapter 5: Manage Multiple Presentations
• About the Author.
• (source: Nielsen Book Data)

Demonstrate your expertise with Microsoft Office! Designed to help you practice and prepare for the 2013 PowerPoint Microsoft Office Specialist (MOS) exam, this all-in-one study guide features: Full, objective-by-objective exam coverage Easy-to-follow procedures and illustrations to review essential skills Hands-on practice tasks to apply what you've learned Includes downloadable practice files.
(source: Nielsen Book Data)

• 1. Computer Abstractions and Technology 2. Instructions: Language of the Computer 3. Arithmetic for Computers 4. The Processor 5. Large and Fast: Exploiting Memory Hierarchy 6. Parallel Processors from Client to Cloud APP A Assemblers, Linkers, and the SPIM Simulator APP B The Basics of Logic Design APP C Graphics and Computing GPUs APP D Mapping Control to Hardware
• APP E A Survey of RISC Architectures for Desktop, Server, and Embedded Computers.
• (source: Nielsen Book Data)

Computer Organization and Design, Fifth Edition, is the latest update to the classic introduction to computer organization. The text now contains new examples and material highlighting the emergence of mobile computing and the cloud. It explores this generational change with updated content featuring tablet computers, cloud infrastructure, and the ARM (mobile computing devices) and x86 (cloud computing) architectures. The book uses a MIPS processor core to present the fundamentals of hardware technologies, assembly language, computer arithmetic, pipelining, memory hierarchies and I/O.Because an understanding of modern hardware is essential to achieving good performance and energy efficiency, this edition adds a new concrete example, Going Faster, used throughout the text to demonstrate extremely effective optimization techniques. There is also a new discussion of the Eight Great Ideas of computer architecture. Parallelism is examined in depth with examples and content highlighting parallel hardware and software topics. The book features the Intel Core i7, ARM Cortex-A8 and NVIDIA Fermi GPU as real-world examples, along with a full set of updated and improved exercises. This new edition is an ideal resource for professional digital system designers, programmers, application developers, and system software developers. It will also be of interest to undergraduate students in Computer Science, Computer Engineering and Electrical Engineering courses in Computer Organization, Computer Design, ranging from Sophomore required courses to Senior Electives.
(source: Nielsen Book Data)