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The sixth chapter explores hyperbolic trees, a variation of radial trees which allows the focus of the visualization to be placed on a single node using layout algorithms. Chapter 7 examines rectangular treemaps, a hierarchical visualization using nested rectangles. The next chapter discusses another variation of treemaps, the circular treemap. Lima notes that these are not as popular as other treemaps due to their ineffective use of space.
Example images include a circular treemap of the market value of public companies featured in the previous chapter. Sunbursts visualizations or radial treemaps are the subject of the penultimate chapter. The Book of Circles by Lima [Lim17] commences with an exploration of circular objects in nature and the use of circles in human history. Circular cities and artefacts, such as Stonehenge in the United Kingdom, circular symbols, the use of circles as metaphors and human preference for curvilinear lines are examined. The second chapter classifies different forms of circular designs into seven families upon which each of the next chapters will be based.
Wheels and pies are presented in the next chapter. These are circular diagrams that have radiating lines from the centre of the circle. The fifth chapter features images combining circular depictions with grid structures. Circular depictions which ebb and flow are the subject of the sixth chapter.
Figures such as radar charts, circular bar charts, radial area charts and radial line charts fall into this category. Visual designs that captivate images of circles within a circle, circular treemaps,and Voronoi patterns are presented in the following chapter. The penultimate chapter looks at circular depictions of geospatial information. Example figures include architectural blueprints, historic maps and other geographical depictions. A detailed description of the visual history of trees and networks is provided. Other than historical aspects, the book does not provide explicit educational function in order to study visualization techniques.
However, multiple designs are presented and due to the large number of examples, the book may be a good source of inspiration. We recommend this book to a wide audience; however, the book is not recommended to those looking for pedagogical guidance in data visualization. Lima does an outstanding job of collecting a wide range of a visual designs and examples inspired by trees and networks.
The Functional Art by Cairo [Cai12] covers a wide range of data visualization topics; however, the focus of the book is towards infographics. A thorough description of visual perception and cognition is provided, as well as tips for creating interactive graphics. Profiles of infographic designers are also given along with examples of their work, a unique feature within all books surveyed. Images are used to support topics discussed in the text; however, The Functional Art does not have as many visualizations as are presented in the Lima books.
We recommend this book to beginners in information visualization, particularly those with an interest in infographics. Different perspectives on data visualization are discussed dependant on the purpose of the visualization and the intended audience. A useful taxonomy of visualizations is presented, showing a number of visualization designs, along with a useful range of digital tools to aid visualization.
The book is written in an informal style; however, it does not feature as many interesting images as the other books within this category. We recommend this book to beginners in data visualization with the book providing a basic guide and a useful list of digital resources. This book is more pedagogical than the others in this category.
It is intended for those that are really interested in applying the guidelines and principles presented. However, it is not in the textbook category because it is for a general audience as opposed to a special target student audience. For each of these data types, a history of visualization is presented along with a number of visualization designs to demonstrate different methods to encode the data.
This book, along with others in this category, does not present information with regard to computational technology and creating digital representations. It is a concise book which can be read quickly, so we recommend this book to readers interested in a quick and casual introduction to information visualization. We also recommend this book to those with interest in visualization designs, such as design students.
Lima's second book within this category, The Book of Trees [Lim14] , is the successor to visual complexity with a stronger focus on tree visualizations. Almost examples are presented in colour, along with a detailed history of tree visualizations. The book is arranged by visualization design such as different forms of treemaps and sunbursts.
Similar to Lima's Visual Complexity , educational content is limited to a historical aspect, but many visualization designs are presented. As with Lima's other books, we recommend this book to a wide audience or for those interested in hierarchical visualization and who also liked his first book. Again, Lima's style focuses on images and examples. This is definitely a strength of each of his books. The Book of Circles [Lim17] is Lima's third book in this survey and follows a similar recipe to the others. Imagery with of circles is the focus of this book with the examples themselves taking centre stage.
A historical aspect is presented along with graphic examples. Visualization fundamentals and other educational aspects are not presented. We recommend this book to a wide audience interested in nice visuals or for those interested in circular designs and readers who enjoyed his two previous books. Books from the textbooks and academic category tend to have a higher number of pages and cite more references in comparison to other books within the survey.
This section contains eight books that are targeted predominantly at data visualization students at the university level. Information Visualization: An Introduction, Third Edition by Spence [Spe14] begins by showing different examples of visual designs from history and explains why information visualization is important—to gain insight.
The third chapter introduces visual layout techniques such as star plots and parallel coordinates. These are then critically analysed with human perception factors in mind. Interaction techniques are briefly discussed and data relationship visualizations are assessed. Display media other than paper or a PC screen are also discussed.
Norman's action cycle is introduced as a model to develop interaction techniques and an example of its use is given in the fifth chapter. The considerations of intuitive design and expected results from interactions are discussed. The author identifies eight explicit steps for creating an interactive information visualization in chapter 6.
Case studies of designing visualizations for various problems are given and critically analysed in the final chapter. A description of videos to compliment topics raised in the book is given in the appendix. Information Visualization: Perception for Design, Third Edition by Ware [War12] considers the importance of human perception when designing visual layouts along with the importance of reducing cognitive load.
Chapter 3 examines contrast, luminance and brightness and the way that they are perceived. The fifth chapter explores visual primitives such as colour, size, shape and orientation. To create distinctive glyphs to stand out amongst others, at least one of those visual primitives must be vastly different. Chapter 7 explores the visualization of data in 3D space. Depth cues such as shading and textures in standard and stereoscopic displays are considered. The perception of surfaces and the positions of data points in space are also examined.
The use of images, text, verbal dialogue and animations in data visualization is scrutinized in the ninth chapter, with the aim of a narrative in mind. The importance of the use of gestures, such as the use of arrows to highlight, is also discussed. The tenth chapter provides a detailed explanation of how data interaction techniques should behave, e.
The thinking process of the user is considered in chapter 11 with emphasis on cognitive load and memory. Example algorithms are given that describe the user interaction with differing visual designs, with the aim of reducing cognitive load.
Ware has published an additional book [War10] in which the perception concepts discussed in Information Visualization: Perception for Design are expanded on to provide advice for designers. The visualization pipeline is introduced and human perception discussed. Chapter 3 deals with human perception of graphics and images specifically in information visualizations. A physiological description of the eye is given along with many figures to highlight perception oddities. Chapter 5 presents different techniques used for scientific data, 1D, 2D and 3D spatial coordinates along with dynamic flow.
The categorization is based on abstract or spatial data, univariate or multivariable data, linear or cyclic time, instantaneous or interval data, static or dynamic visual designs, and 2D or 3D visualizations. Chapter 10 introduces text and document visualization techniques and some example approaches such as word clouds and arc diagrams are given.
Chapter 11 introduces different classes of interaction techniques: navigation, selection, filtering, reconfiguring, encoding, connecting, abstracting as well as combinations of those classes. Interaction spaces are also identified as screen, data value, data structure, attribute, object or visualization structure.
Chapter 12 discusses and exemplifies algorithms used for each of the interaction spaces identified in the previous chapter. Algorithms for animating the interaction and interaction control principles are also discussed. In chapter 13, guidance is given for designing visualizations such as data mapping, selecting views, information density, labelling, use of colour and the importance of aesthetics.
Potential problems that may occur when producing visual layouts are also identified. The importance of identifying the purpose and the audience of the visualization are highlighted in chapter 14, along with data characteristics and image characteristics in order to benchmark different visualizations. Active research fields in data visualization are highlighted in the final chapter. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Yau [Yau11] begins by explaining that visualization is useful to present numerical data in an engaging manner, and to show patterns and relationships.
Basic principles are outlined for creating visual designs such as labelling axes and data correctly, citing data sources, and to consider the target audience. The second chapter discusses the data used to create the visual layout, where to find data sources and how to format the data.
Example tools used to visualize data are presented in the next chapter. The fifth chapter concerns graphics that use proportional representations, such as pie charts, stacked bar graphs, treemaps and stacked area graphs. Some R code examples are provided for their creation. Visual designs for finding data relationship are discussed in chapter 6. Heatmaps, Chernoff faces, star charts and parallel coordinates are used as examples along with R code for their creation. Dimensionality reduction is also briefly addressed.
The penultimate chapter is based on geographic data. Yau also has a related book called Data Points: Visualization That Means Something which focuses more on the graphical side of data visualization [Yau13]. Interactive Visualization: Insight Through Inquiry by Bill Ferster [FS12] commences with different genres of the topic and describes the historic advancements with the introduction of computers and the internet. The chapter discusses how to create a solid question to drive the development of a visual design. Considerations are made to the intended audience and their expertise, and for creating a focused question.
The difference between primary, secondary and tertiary sources of data is explained, and data quality is discussed and example sources of data are provided. Quantitative versus qualitative information is explained and data structures and comparison with other data sets are discussed. Chapter 5 concerns the Envision section of the ASSERT model which examines strategies for analysing and representing the data to answer the question. Analysis techniques are discussed for quantitative data such as relationships, and qualitative data such as frequency analysis. Many aspects to be considered when creating imagery are discussed such as perception and cognition, usability, aesthetics, use of colour and spatial arrangement.
Different display strategies are discussed and a number of tools that can be used to create visual depictions are presented such as Wordle [Fei14]. The use of a storytelling component in a visualization is delineated and examples of storytelling visualizations such as Minard's map of Napoleon's march to Moscow are given before a discussion of misleading representations.
The ninth chapter discusses the technology behind the Internet and how it is accessed including the use of Adobe's Flash and vector graphics. Chapter 11 provides guidance on using spreadsheet programmes such as Microsoft Excel and Google Docs. Visualization Analysis and Design by Munzner [Mun14] begins with a discussion of why computer graphics for data is needed, for the discovery of knowledge in data, and gives a broad overview of considerations, such as resources available, for creating visual designs.
Data types, their format and their sources are categorized and explained in the second chapter. The third chapter explores the importance of considering the reasons for creating visual representations, who the user is and what goals the imagry is trying to achieve. This is to ensure that the appropriate design choices are made when creating a visual design. Examples are given of visual designs created using derived data. Chapter 4 describes four distinct levels for creating a visual design, identifying the requirements of the visual design, abstracting the data, designing the visual layout and interaction and encoding the visual design computationally.
A detailed analysis is also made up of the effectiveness of the different attributes with a consideration of human perception. Chapter 6 presents eight rules of thumb for consideration when designing visual layouts along with detailed justification of each rule. Example rules of thumb include the use of Shneiderman's information seeking mantra [Shn96].
Visualization techniques for data found in tabular form, i. Spatially ordinated data visualizations are considered with examples given in chapter 8. These include geographical data as well as scalar and vector fields. Hierarchical visual designs examples are also provided including treemaps. The use of colour to map data attributes is studied in detail in chapter 10, in particular, the perception of luminance, hue and saturation used in different scenarios. The use of other attributes such as shape, size and texture is also mentioned.
Interaction techniques such as highlighting elements and changing the viewpoint by zooming and scaling are described in chapter 11 along with cutting and slicing of 3D scenes to produce 2D visualizations. Considerations of dissecting data to create multiple views are discussed in chapter Adjacent views and layered views are presented with multiple programmes using the techniques described.
Chapter 13 presents options for reducing data by filtering. Three broad methods are described: reducing the number of items, reducing the number of attributes and grouping data. Examples of applications that use these techniques are given. Focus and context techniques are split into three categories in chapter superimposing layers, geometry distortion and filtration and aggregation.
Data Visualisation: A Handbook for Data Driven Design by Kirk [Kir16] begins by defining data visualization as The representation and presentation of data to facilitate understanding , and presents three principles for good visualization design, to be trustworthy, accessible and elegant. The second chapter outlines a workflow and describes a mindset for creating an information visualization design. Chapter 3 discusses the initial stage of creating a design, formulating the design brief. Considerations of the audience, constraints such as time, available technology and required deliverables are discussed.
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A discussion is made up of different data types, textual, nominal, ordinal, interval and ratio in chapter 4. Chapter 5 presents the design approach to visualization, the editorial angle, what data are included and what the focus of the visualization is. Examples of visual designs are studied to demonstrate the editorial aspect such as a series of charts showing National Football League touchdown passes.
Interaction options are split into two features: data adjustment and presentation adjustment in chapter 7. Examples of data adjustment include filtering, animating and data exploration, whilst examples of presentation adjustment are visual emphasis, annotations and orientation. The use of annotations is discussed in chapter 8. An overview of colour theory is given in chapter 9 before a discussion of the most appropriate use of colour for data legibility and emphasis. The penultimate chapter discusses the composition and layout of a visualization.
In the final chapter, a critical analysis of a visual design is provided, highlighting the techniques made throughout the book. The layout of the book consists of three parts matching the process of developing a tool: think, prepare and sketch. As the name suggests, the method involves five large sides of paper, the first for sketching ideas, the second, third and fourth for different design solutions and the fifth for a sketch of the final design.
The structure of the sheets is described and the use of the Five Design Sheets method in different scenarios is detailed. Chapter 3 discusses different forms of problems, characterizing them and various ways of thinking to address a variety of problems. Social and ethical considerations are studied in the fourth chapter. The fifth chapter looks at sketching skills and techniques. The use of sketching in planning is highlighted and uses of colour and line thickness to enhance sketches is explained.
Tips to improve sketching skills are also provided with a recommendation to create a sketching kit. Chapter 6 explores the Gestalt principles and how humans interpret graphical marks on a page. Methods to help creative thinking and generate ideas are presented in the seventh chapter such as taking inspiration from nature and being well rested. Sheets 2—4 of the method are presented in chapter 9, which describes each of the three design solutions. Each design should be carefully considered and detailed with pros and cons for each design as well as how the user will interact with the design.
The final sheet, detailed in chapter 10, features one of the previous three designs taken forward as a final solution. Spence's book Information Visualization: An Introduction is aimed at university students from any discipline who need to visualize data. The book focuses on creating a visual design whilst presenting data visualization principles. Exercises are also given at the end of each chapter. Some unique topics discussed in this book are eye tracking and gaze heat maps as well as alternative canvases.
Spence's book is the first academic text book on information visualization and thus features early research work in this field. Information Visualization: Perception for Design by Ware provides a comprehensive analysis of human perception and vision with an emphasis on data visualization. Although other books within this survey discuss perception, none are as detailed as Ware's work, and often reference Ware's work themselves. Other topics within information visualization are not discussed in much detail within this book due to its strong focus on visualization perception.
Like some other books in this category, exercises are presented for university students to complete. A topic unique to this book in this category is research directions, where future research topics are explored. We recommend for those with some prior knowledge of computer science. This book is more technical than other books in this section, with the exception of the focus of Ware's book. This is actually the textbook we recommend as a starting point for university computer science students in data visualization. It is a good starting point. The book is written in a casual and informal style.
We recommend this book for beginners in visualization who are looking to quickly create imagery, with the book focusing on producing visual designs over educational content. We also recommend this for university students that are not necessarily computer science students. Interactive Visualization: Insight Through Inquiry by Ferster provides a framework for creating a visualization design, based on the data visualization principles.
Of particular use are eight rules of thumb that Munzner presents for the creation of a visualization. Kirk's Data Visualisation: A Handbook for Data Driven Design provides another perspective for creating a visualization design from concept to realization, similar to Spencer's, Ferster's and Yau's books. An easy and informal style is used. A taxonomy of 49 different visual designs is a useful resource available in this book; however, fewer images are used overall.
Exercises are available with the book, but only from an online resource. We recommend Kirk's book to students that are less interested in the research aspects of data visualization and are more interested in the contemporary aspects and culture. This book differs from the others in this category by not focusing on visualization design principles. We recommend this book to readers who are interested in deriving and brainstorming visual designs at the conceptual level. This book goes into the most depth on how to come up with a range of visual designs centred around addressing a specific challenge.
Comparison of supplementary material A number of books within this survey complement the printed volume with additional supplementary material, this is especially true for textbooks where five of the eight books provide additional material. The supplementary material, where available, varies from details of data sets referenced in the books to supporting videos. Supplementary material is usually provided on a dedicated website allowing for corrections to errata as they become apparent and the addition of new material.
However, websites can sometimes be removed with the loss of the supplementary material as with the web address referenced in Illuminating the Path [CT05]. Seven books are identified in this category, books aimed at professionals from any industry their covers can be seen in Figure 9. These differ from textbooks as they do not provide as much background important in a pedagogical context.
Show Me the Numbers: Designing Tables and Graphs to Enlighten by Few [Few12] commences with the purpose of the book which is to enable the reader to produce effective means of displaying quantitative information. The second chapter examines different types of data. The difference between quantitative and categorical data is explored along with different types of relationships, for example, nominal, hierarchical and correlation.
Statistical methods such as mean, median and standard deviation are also explained. Chapter 3 discusses the use of tables and graphs for displaying data and when to use each. Tables are used when precise, individual values are required or when more than one unit of measure is used, whilst graphs are used to display the shape of the data or to reveal relationships. A brief history of the use of graphs is also given with references to the work of Descartes [Des37] and Playfair [Pla86].
The use of tables is also discussed in the fourth chapter. Table layouts and designs are explained for different purposes and data. The fifth chapter discusses human perception of shapes and marks. A description is given of how the eye and brain processes images and how this influences the perception of length, width, size, shape, colour and position as a means of displaying quantitative information.
The Gestalt principles of visual perception are also discussed. Different methods of encoding data such as points, lines and colour are introduced along with different graph relationships such as time series, distributions and geospatial in chapter 6. Graph designs for each type of relationship are presented. An additional section appears at the end of the chapter with six scenarios of different types of data, where the reader is tasked with identifying the most suitable visualization method.
Chapter 7 outlines design features of a visualization such as the layout of a figure, the use of titles, legends and labels and the use of highlighting and emphasis. The design features specific to tables are discussed in the eighth chapter. The use of grids, white space and fill colour to delineate columns and rows, the arrangement of the data, formatting of text and the use of summarizing values are examined. Chapter 9 meanwhile features general design features of graphs.
The importance of quantitative scales that start at zero and of consistent scale spacing is highlighted, along with the interpretation challenges with using 3D layouts. The tenth chapter continues with a description of the format of more specific design features of graphs such as graph marks, points, lines and bars. Displaying multiple variables in one figure is the topic of chapter This is achieved with the use of small multiples.
The technicalities of the layout, sequencing and general use of the small multiples are studied. Chapter 12 highlights graphs that should be avoided due to their inability to communicate data effectively. These include doughnut charts, radar charts and circle charts along with the reasoning behind their dismissal. Storytelling is the focus of chapter Few present characteristics that effective stories have in common, for example, they are simple, informative and contextualized. Information Dashboard Design: The Effective Visual Communication of Data by Few [Few06] begins with a brief history of Business Intelligence BI data visualization and defines an information dashboard: A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.
Few explain that most information dashboards used in business fall short of their potential and provide a number of examples of business dashboards. Different types of information that are typically used in dashboards are also explored. Common mistakes in dashboard design are the focus of the third chapter. Examples from vendor websites are used to highlight design mistakes such as choosing inappropriate graphics, visual designs cluttering displays with decoration and fragmenting data into separate screens.
Human perception is the topic of the next chapter. Using the best type of visual layout for the information to be displayed is the focus of the sixth chapter. A number of visual layout types are given such as bullet graphs, bar charts, scatter plots and treemaps. Chapter 7 discusses designing dashboards for usability. Effective organization of the information is considered along with making the dashboard aesthetically pleasing and maintaining consistency of colours and interaction.
User testing of the design is recommended. The final chapter provides examples of dashboards in different scenarios. Examples dashboards are given for telesales, marketing, sales and for use by a chief information officer. Eight examples of sales dashboards are also given for critique.
The first chapter discusses the changing landscape of IT. Changes in the way people interact with IT and how technology companies are adapting by creating new tools and collection data. Data visualization tools for businesses are explored in the second chapter. Dedicated visualization tools such as Tableau, Microsoft Excel and visualization tools incorporated within business intelligence software are examined.
The ability of visualization tools to integrate with databases and statistical software and their ease of use are discussed. The third chapter studies the TV and film streaming site Netflix and its use of data visualization. The extent to which Netflix utilizes the data it collects to provide insights into its customers and to improve the service it offers is examined. The online polling company Wedgies is the subject of the fourth chapter.
The sixth chapter introduces four levels of organizations categorized by the data that they visualize. The third and fourth levels utilize big data sets using static and interactive techniques, respectively. Chapter 7 provides an example of human resource visual design at Autodesk. A visualization shows the personnel changes over time, produced using Java, Processing and Graph Viz.
The broad outline to utilize visualizations within business is presented in the eighth chapter. Obtaining the data, processing it, and utilizing available tools such as Hadoop or Amazon Web Services is the first step. Considering what kind of visual layout is required for individual scenarios is an important factor along with visualization design, and user experience.
The penultimate chapter looks at common pitfalls for organizations that may try to utilize visualization. Details are also given on how to effectively round numbers. Presenting numeric data in tables is the subject of the second chapter. Chapter 3 discusses the use of charts. Principles are outlined for the correct use of bar charts, histograms, pie charts and graphs. Multiple examples of charts are given along with how they could be improved, for example, by ensuring axes start at zero.
Numbers in text is the subject of the fourth chapter, such as in headlines or statements. The final chapter discusses using the Internet as a form of sharing data. Some history of national statistics data on the Internet is given along with some examples of current designs such as InfoBase Cymru [Dat]. The first chapter discusses the importance of understanding the purpose of a visual design and to consider the audience before its creation.
Chapter 2 describes different forms of graphic visualizations such as bar graphs, scatter plots and line graphs. The third chapter discusses the issue of clutter, defined as visual elements that do not contribute to an increased understanding of data. Gestalt principles of visual perception are explored and used in an example where clutter is removed from a scatter graph. The fifth chapter discusses the use of design principles for creating a visual layout. Chapter 7 discusses how to build an effective story, using a beginning, a middle and an ending.
The order of the narrative of the story is also discussed.
Chapter 9 addresses five situations that have not been addressed so far in the book, visualizations with a dark background, producing a static figure of an animated visualization, ordering in categorical data, avoiding over complex line graphs and alternatives to pie charts. Berinato discusses what features tend to stand out in a visualization, such as a peak on a line graph, and how too much information makes it difficult to find individual data points. The skills and tools required to produce the visual design can be determined by first identifying the type of visual design.
Refining visual layouts to make them visually appealing is the topic of the fifth chapter. CDD official Franklin Oduro said the organization was able to help the electoral commission by alerting it to supply shortages at polling sites. In other elections, we used laptops to build a database of information sent in by monitors speaking over mobiles, but it was a pretty slow process. IRI has deployed dozens of teams to monitor foreign elections, many of which used satellite phones to gather information about polling station conditions.
Shawn Beighle, IRI's director of ICT, said that in elections in Nigeria, Georgia, Kenya, Ukraine and Bangladesh, "we used satellite phones because in a lot of the countries we can't trust the cellular networks to be functional. Some countries even shut down cell phone networks during elections.
They were given a one-page cheat sheet of monitoring questions to which they would phone in quick answers to an integrated voice response [IVR] system operated by the command center in Abuja. Skip to main content. Half Beer - Beer glass classifier created with Synaptic. PGM - A Julia framework for probabilistic graphical models.
Regression - Algorithms for regression analysis e. Neural - A neural network in Julia. GLM - Generalized linear models in Julia. Gaussian Processes - Julia package for Gaussian processes. Clustering - Basic functions for clustering data: k-means, dp-means, etc.
MultivariateStats - Methods for dimensionality reduction. NMF - A Julia package for non-negative matrix factorization.
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ANN - Julia artificial neural networks. ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction. Flux - Relax! LightGraphs - Graph modeling and analysis. Julia Data - library for working with tabular data in Julia. Hypothesis Tests - Hypothesis tests for Julia.
Gadfly - Crafty statistical graphics for Julia. Stats - Statistical tests for Julia. RDataSets - Julia package for loading many of the data sets available in R. DataFrames - library for working with tabular data in Julia. Distributions - A Julia package for probability distributions and associated functions.
Data Arrays - Data structures that allow missing values. Sampling - Basic sampling algorithms for Julia. SignalProcessing - Signal Processing tools for Julia. Images - An image library for Julia. DataDeps - Reproducible data setup for reproducible science. It is used, among many other places, at the heart of SciPy. Inspired by the original Python version. It emphasizes flexibility through the elegant use of object-oriented design patterns. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.
This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos. A library for finding interest points based on fast integral histograms. Curvelets - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
Spider - The spider is intended to be a complete object orientated environment for machine learning in Matlab. Pattern Recognition Toolbox - A complete object-oriented environment for machine learning in Matlab. Pattern Recognition and Machine Learning - This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C.
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Optunity - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. NET applications. Development has now shifted to GitHub. Some components are also available for Java and Android. Natural Language Processing Stanford.
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NLP for. NET and also available precompiled as a NuGet package. NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications. This package is part of the Accord. NET Framework. DiffSharp - An automatic differentiation AD library providing exact and efficient derivatives gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products for machine learning and optimization applications.
Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use of differentiation, for applications such as hyperparameter optimization. GeneticSharp - Multi-platform genetic algorithm library for. NET Core and. The library has several implementations of GA operators, like: selection, crossover, mutation, reinsertion and termination. NET - Infer. NET is a framework for running Bayesian inference in graphical models. One can use Infer. NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.
NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. NET - ML. NET is a cross-platform open-source machine learning framework which makes machine learning accessible to. NET developers.
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NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.
NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Net 4. Net 3. Sho - Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts in IronPython with compiled code in. NET to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any.
NET language, as well as a feature-rich interactive shell for rapid development. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple's Accelerate Framework, using vectorized operations and hardware acceleration if available.
Includes sample code for use from Swift. This network can be used in products recommendation, user behavior analysis, data mining and data analysis. KRHebbian-Algorithm - It is a non-supervisor and self-learning algorithm adjust the weights in neural network of Machine Learning. It could be used in data mining and image compression. Libra-Tk - Algorithms for learning and inference with discrete probabilistic models.
PredictionBuilder - A library for machine learning that builds predictions using a linear regression. SimpleCV - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. OpenFace - Free and open source face recognition with deep neural networks. PCV - Open source Python module for computer vision. It is written in Python and powered by the Caffe2 deep learning framework. Supports classification, segmentation, detection out of the box.
That is, it will recognize and "read" the text embedded in images. Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others. Quepy - A python framework to transform natural language questions to queries in a database query language.
YAlign - A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora. General purpose NLP library for Python. Distance - Levenshtein and Hamming distance computation. Polyglot - Multilingual text NLP processing toolkit. DrQA - Reading Wikipedia to answer open-domain questions. Dedupe - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution. Introduces very simple interface that enables clean machine learning pipeline design. Documentation can be found here. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results.
Corresponding dataset s are stored into a SQL database, then generated model s used for prediction s , are stored into a NoSQL datastore. Featureforge A set of tools for creating and testing machine learning features, with a scikit-learn compatible API. SimpleAI Python implementation of many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.
BigML - A library that contacts external servers. Pylearn2 - A Machine Learning library based on Theano. Lasagne - Lightweight library to build and train neural networks in Theano. Brainstorm - Fast, flexible and fun neural networks. This is the successor of PyBrain. Surprise - A scikit for building and analyzing recommender systems. Crab - A flexible, fast recommender engine.
Image-to-Image Translation with Conditional Adversarial Networks - Implementation of image to image pix2pix translation from the paper by isola et al.