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10 Fresh jQuery Plugins for Designers & Developers

10 Fresh jQuery Plugins for Designers & Developers | Best | Scoop.it
Latest jQuery Plugins for designers & developers. Today I selected fresh jQuery plugins which can help to develop your project must faster and easier.
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Brian Granger, Chris Colbert & Ian Rose - JupyterLab+Real Time Collaboration

At their PyData Seattle talk on Jupyter Lab, the authors demonstrate opening a 1 trillion row by 1 trillion column csv (and effortlessly scrolling left and right across the columns), as well as realtime collaboration using the Jupyter Lab Google Drive extension, OOTB Vega and GeoJSON compatibility, and plenty of other incredible features.

 
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Python Virtual Environments: A Primer

Python Virtual Environments: A Primer | Best | Scoop.it
This article details how to use a Python virtual environment to manage your Python projects.
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UCSD Analyzed 1 Million Jupyter Notebooks — Now You Can Too

UCSD Analyzed 1 Million Jupyter Notebooks — Now You Can Too | Best | Scoop.it

In July 2017, the Design Lab at UC San Diego scraped and analyzed over 1 million Jupyter Notebooks from GitHub. They are making these data publicly available for everyone to explore! While only a snapshot of one corner of the Jupyter universe, these data provide unique perspective into how people use and share Jupyter Notebooks.

 

The collection includes over 1 million notebooks as well as metadata about the nearly 200,000 repositories where they lived. The full dataset is nearly 600GB so we have created a smaller 5GB sampler dataset for you to get started. This includes roughly 6,000 notebooks from 1000 repositories.

 

They originally collected these data to explore how people use narrative text in Jupyter Notebooks. The UCSD team found many notebooks, even those accompanying academic publications, had little in the way of descriptive text. This is likely because many analysts view their notebooks as personal and messy works-in-progress. On the other hand, many of the notebooks they collected were masterpieces of computational narrative, elegantly explaining complex analyses (one notebook even had more text than The Great Gatsby). The UCSD team members think this spread reflects a tension between data exploration, which tends to produce messy notebooks, and process explanation, in which analysts clean and organize their notebooks for a particular audience.

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Seeing Theory — Visual Tutorials on Probability and Statistics

Seeing Theory — Visual Tutorials on Probability and Statistics | Best | Scoop.it

"As mathematics instructors and students know, lucid visualizations are essential to helping learners understand complex mathematical concepts. Seeing Theory is an online, interactive textbook that utilizes colorful, interactive visualizations and animations to explain concepts like compound probability and Bayesian Inference. This resource was envisioned by Daniel Kunin (currently a master's student in mathematics and computer science at Stanford University), who created Seeing Theory along with designer Jingru Guo, software engineer Tyler Dae Devlin, and statistics student Daniel Xiang.

 

Seeing Theory contains six chapters, each of which contains three interactive visualizations. Each visualization contains two panels: a short explanation of each concept appears on the left, while a graph or chart appears on the right. In addition, the left panel often contains an interactive element. For instance, in the basic probability module, users are invited to flip a coin, roll a die, and draw a card. As they do so, the graph on the right reflects the outcome of these actions, revealing the principles of basic probability."


Via Jim Lerman, Dennis Swender
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Rescooped by Dr. Stefan Gruenwald from Pathogens, speciation, domestication, genomics, fungi, biotic interactions
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Making data come alive with Circos Plots

Making data come alive with Circos Plots | Best | Scoop.it

Circos plots are a great way to show genomic and other data and are famous (and infamous!) for their ability to show several different data types across dozens of chromosomes in a single plot. But it isn’t always easy to make these plots — this article covers some of your best options.


Via Pierre Gladieux
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30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets

30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets | Best | Scoop.it

This collection of data science cheat sheets is a curated list of reference materials spanning a number of disciplines and tools.

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30+ Real Examples Of Blockchain Technology In Practice

30+ Real Examples Of Blockchain Technology In Practice | Best | Scoop.it
When many people think of blockchain technology, the first thing that comes to mind is Bitcoin. But in the last several years, blockchain isn’t only enabling cryptocurrencies, it is revolutionizing many industries. Here we take a look at some of the most practical uses of blockchain technology.
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How to build a chatbot interface

How to build a chatbot interface | Best | Scoop.it
Here are the tools and techniques you need to build a chatbot.

 

In the mid-2000s, virtual agents and customer service chatbots received a lot of adulation, even though they were not very conversational, and under the hood they were merely composed of data exchanges with web servers. 

 

Nowadays, even though a huge number of examples of ‘weak AI’ exist (including Siri, Alexa, web search engines, automated translators and facial recognition) and other topics such as responsive web design are hogging the limelight, chatbots are still causing a stir. With major investment from big companies, there remain plenty of opportunities to hack the conversational interfaces of the future.

 

 

Sometimes they get a bad reputation, but chatbots can be useful. They don’t need to feel like a basic replacement for a standard web form, where the user fills in input fields and waits for validation – they can provide a conversational experience. 

 

Essentially we’re enhancing the user experience to feel more natural, like conversing with an expert or a friend, instead of web browser point-and-clicks or mobile gestures. The aim is that by providing empathetic, contextual responses, this technology will become embedded directly in people’s lives.

 

Watch the video or read on to discover a practical way to design and build a chatbot, based on a real project-intake application in a service design practice.

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A Guide to Scientific Python Plotting With Matplotlib

A Guide to Scientific Python Plotting With Matplotlib | Best | Scoop.it

This article is a beginner-to-intermediate-level walkthrough on Python and matplotlib that mixes theory with examples.

 

A picture says a thousand words, and with Python’s matplotlib library, it fortunately takes far less than a thousand words of code to create a production-quality graphic.

 

However, matplotlib is also a massive library, and getting a plot to look “just right” is often practiced on a trial-and-error basis. Using one-liners to generate basic plots in matplotlib is fairly simple, but skillfully commanding the remaining 98% of the library can be daunting.

 

This guide is a beginner-to-intermediate-level walkthrough on matplotlib that mixes theory with example. While learning by example can be tremendously insightful, it helps to have even a surface-level understanding of the library’s inner workings and layout as well.

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PyTorch, Dynamic Computational Graphs and Modular Deep Learning

PyTorch, Dynamic Computational Graphs and Modular Deep Learning | Best | Scoop.it

Deep Learning frameworks such as Theano, Caffe, TensorFlow, Torch, MXNet and CNTK are the work horses of Deep Learning work. These frameworks as well as the GPU (predominantly Nvidia) are the what enables the rapid growth of Deep Learning. It was refreshing to hear Nando de Freitas acknowledge their work in the recently concluded NIPS 2016 conference. Infrastructure does not get enough of the recognition it deserves in the academic community. Yet, programmers toil on to continually tweak and improve their frameworks.

 

Recently, a new framework was revealed by Facebook and a bunch of other partners (Twitter * NVIDIA * SalesForce * ParisTech * CMU * Digital Reasoning * INRIA * ENS). PyTorch came out of stealth development. PyTorch is an improvement over the popular Torch framework (Torch was a favorite at DeepMind until TensorFlow came along). The obvious change is the support of Python over the less often used Lua language. Almost all of the more popular frameworks use Python, so it is a relief that Torch has finally joined the club.

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Jupyter is where humans and data science intersect

Jupyter is where humans and data science intersect | Best | Scoop.it

Recent beta release of JupyterLab embodies the meta-theme of extensible software architecture for interactive computing with data. While many people think of Jupyter as a “notebook,” that’s merely one building block needed for interactive computing with data. Other building blocks include terminals, file browsers, LaTeX, markdown, rich outputs, text editors, and renderers/viewers for different data formats. JupyterLab is the next-generation user interface for Project Jupyter, and provides these different building blocks in a flexible, configurable, customizable environment. This opens the door for Jupyter users to build custom workflows, and also for organizations to extend JupyterLab with their own custom functionality.

 

Thousands of organizations require data infrastructure for reporting, sharing data insights, reproducing results of analytics, etc. Recent business studies estimate that more than half of all companies globally are precluded from adopting AI technologies due to a lack of digital infrastructure — often because their efforts toward data and reporting infrastructure are buried in technical debt. So much of that infrastructure was built from scratch, even when organizations needed essentially the same building blocks. JupyterLab’s primary goal is to make it routine to build highly customized, interactive computing platforms, while supporting more than 90 different popular programming environments.

 

A major theme builds on top of the other two: computational communication. For data and code to be useful for humans, who need to make decisions, it has to be embedded into a narrative — a story — that that can be communicated to others. Examples of this pattern include: data journalism, reproducible research and open science, computational narratives, open data in society and government, citizen science, and really any area of scientific research (physics, zoology, chemistry, astronomy, etc.), plus the range of economics, finance, and econometric forecasting.

Another growing segment of use cases involves Jupyter as a “last-mile” layer for leveraging AI resources in the cloud. This becomes especially important in light of new hardware emerging for AI needs, vying with competing demand from online gaming, virtual reality, cryptocurrency mining, etc.

 

Take the following as personal opinion, observations, perspectives: We’ve reached a point where hardware appears to be evolving more rapidly than software, while software appears to be evolving more rapidly than effective process. O’Reilly Media work to map the emerging themes in industry, in a process nicknamed “radar”. This perspective about hardware is a theme I’ve been mapping, and meanwhile comparing notes with industry experts. A few data points to consider: Jeff Dean’s talk at NIPS 2017, “Machine Learning for Systems and Systems for Machine Learning” about comparisons of CPUs/GPUs/TPUs, and how AI is transforming the design of computer hardware; The Case for Learned Index Structures, also from Google, about the impact of “branch vs. multiple” costs on decades of database theory; this podcast interview “Scaling machine learning” with Reza Zadeh about the critical importance of hardware/software interfaces in AI apps; the video interview that Wes McKinney and I recorded at JupyterCon 2017 about how Apache Arrow presents a much different take on how to leverage hardware and distributed resources.

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Turning Design Mockups Into Code With Deep Learning

Turning Design Mockups Into Code With Deep Learning | Best | Scoop.it
Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code.

Currently, the largest barrier to automating front-end development is computing power. However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now.

In this post, we’ll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup.
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The AI 100: Artificial Intelligence Startups That You Better Know

The AI 100: Artificial Intelligence Startups That You Better Know | Best | Scoop.it

SoundHound NEWS & MEDIA

Endgame CYBERSECURITY

Invoca MARKETING, SALES, CRM

C3 IoT IOT

InsideSales.com MARKETING, SALES, CRM

CrowdFlower ENTERPRISE AI

Narrative Science CROSS-INDUSTRY

ZestFinance FINTECH & INSURANCE

Aquifi COMMERCE

CrowdStrike CYBERSECURITY

Anki ROBOTICS

Shape Security CYBERSECURITY

Appthority CYBERSECURITY

Versive CYBERSECURITY

WorkFusion RISK & REGULATORY COMPLIANCE

Upstart FINTECH & INSURANCE

Vicarious Systems ROBOTICS

Captricity CROSS-INDUSTRY

Trifacta ENTERPRISE

Flatiron Health HEALTHCARE

Benson Hill Biosystems AGRICULTURE

Brain Corp ROBOTICS

MAANA IOT

Socure RISK & REGULATORY COMPLIANCE

Affirm FINTECH & INSURANCE

Sherpa PERSONAL ASSISTANTS

Dynamic Yield COMMERCE

Conversica MARKETING, SALES, CRM

Reflektion COMMERCE

MOOGsoft IT & NETWORKS

Cybereason CYBERSECURITY CRV

DataRobot ENTERPRISE AI

Onfido RISK & REGULATORY COMPLIANCE

Face++ CROSS-INDUSTRY

Casetext LEGAL TECH 8VC

Darktrace CYBERSECURITY

Algolia ENTERPRISE AI

AEYE AUTO TECH

Mobvoi CROSS-INDUSTRY

OrCam Technologies IOT

Recursion Pharmaceuticals HEALTHCARE

Insilico Medicine HEALTHCARE

Neurala ROBOTICS

Mya Systems HR TECH

FLYR TRAVEL

AiCure HEALTHCARE

Zymergen LIFE SCIENCE

Tamr ENTERPRISE AI

Bytedance NEWS & MEDIA

Appier COMMERCE

Applitools SOFTWARE DEVELOPMENT & DEBUGGING

Orbital Insight GEOSPATIAL ANALYTICS

Preferred Networks IOT

Liulishuo EDUCATION

Osmo EDUCATION

Shift Technology CYBERSECURITY

Mighty AI AUTO TECH

Textio HR TECH

Descartes Labs GEOSPATIAL ANALYTICS

Text IQ RISK & REGULATORY COMPLIANCE

Tractable CROSS-INDUSTRY

Kyndi CROSS-INDUSTRY

SPORTLOGiQ SPORTS

Twiggle COMMERCE

NAUTO AUTO TECH

Workey HR TECH

Arterys HEALTHCARE

babylon HEALTHCARE

UBTECH Robotics ROBOTICS

CognitiveScale CROSS-INDUSTRY

Cape Analytics FINTECH & INSURANCE

Numerai FINTECH & INSURANCE

PerimeterX CYBERSECURITY

SparkCognition CYBERSECURITY

Drive.ai AUTO TECH

CloudMinds ROBOTICS

Foghorn Systems IOT

Zoox AUTO TECH

Shield AI PHYSICAL SECURITY

Freenome HEALTHCARE

Gong MARKETING, SALES, CRM

Amplero MARKETING, SALES, CRM

Prospera AGRICULTURE

LeapMind ENTERPRISE AI

Mobalytics E-SPORTS

Insight Engines CROSS-INDUSTRY

Kindred Systems ROBOTICS

Graphcore $ 110 HARDWARE FOR AI

Petuum ENTERPRISE

Mythic HARDWARE FOR AI

Element AI ENTERPRISE AI

SenseTime CROSS-INDUSTRY

Cerebras Systems HARDWARE FOR AI

Afiniti MARKETING, SALES, CRM

Deep Sentinel PHYSICAL SECURITY

Merlon Intelligence RISK & REGULATORY COMPLIANCE

Obsidian Security CYBERSECURITY

Cambricon HARDWARE FOR AI

Tempus Labs HEALTHCARE

Primer CROSS-INDUSTRY

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Applied Data Science – Building Your Own Deep Learning System

Applied Data Science – Building Your Own Deep Learning System | Best | Scoop.it
Cutting edge data science projects.
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9 Apps To Easily Make 3D Printable Objects

9 Apps To Easily Make 3D Printable Objects | Best | Scoop.it

One of the problems with 3D printing is getting a hold of things to print. You can of course download pre-made objects from a variety of places like Thingiverse; but if you want something unique and made by you, that’s where things get a little difficult. Here are 9 quick and easy apps for making something a little more unique.

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Rescooped by Dr. Stefan Gruenwald from JavaTpoint
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SVG Tutorial for beginners

SVG Tutorial for beginners | Best | Scoop.it
SVG Tutorial for beginners and professionals with Introduction, First Example, Basic Shapes, Text, Stroke, g Element, defs Element, Symbol Element, Filters, Blur Effect, Drop Shadow Effect, Pattern, Gradients, Linear Gradients, Animation, Clip Path, Radial Gradients etc.

 

SVG tutorial provides basic and advanced concepts of SVG graphics in XML format. This SVG tutorial is designed for beginners and professionals alike.

 

SVG is a graphics in XML format language which is developed Google with the vision of fast development and high performance. This SVG Tutorial includes all topics of SVG graphics in XML format such as introduction, first example, basic shapes, text, stroke, g element, defs element, symbol element, filters, blur effect, drop shadow effect, pattern, gradients, linear gradients, animation, clip path, radial gradients etc.

Prerequisite

Before learning SVG, you must have the basic knowledge of SVG graphics in XML format.


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Basic data analysis on Twitter with Python

Basic data analysis on Twitter with Python | Best | Scoop.it

After creating the Free Wtr bot using Tweepy and Python and this code, the author wanted a way to see how Twitter users were perceiving the bot and what their sentiment was. So he created a simple data analysis program that takes a given number of tweets, analyzes them, and displays the data in a scatter plot.

 

In order to create this, you have to install a few packages, including  Tweepy , Tkinter , Textblob and  matplotlib . These packages can be installed using the pip package manager.

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25 Open Datasets for Deep Learning Every Data Scientist Must Work With

25 Open Datasets for Deep Learning Every Data Scientist Must Work With | Best | Scoop.it

The key to getting better at deep learning (or most fields in life) is practice. Practice on a variety of problems – from image processing to speech recognition. Each of these problem has it’s own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.

 

This review lists a collection of high quality datasets that every deep learning enthusiast should work on to apply and improve their skill set.  Included are papers with state-of-the-art (SOTA) results to improve models.

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A Curated Selection of Data Visualization Charts and Infographics: The Information Is Beautiful Awards

A Curated Selection of Data Visualization Charts and Infographics: The Information Is Beautiful Awards | Best | Scoop.it

Robin Good: David McCandlees, the author of the book Information is Beautiful celebrates great data visualization and information design work through the Information is Beautiful Awards.

Together with a jury of experts like Brian Eno, Paola Antonelli, Maria Popova, Simon Rogers and Aziz Kami, he has curated a unique selection of 300 designs and a short list of finalists in the following categories:

 

» Data visualization– A singular visualisation of data or information.» Infographic – Using multiple data visualisations in service to a theme or story

 

» Interactive visualization – Any viz where you can dynamically filter or explore the data.

 

» Data journalism – A combination of text and visualizations in a journalistic format.

 

» Motion infographic – Moving and animated visualizations along a theme or story.

 

» Tool or website – Online tools & apps to aid datavizzing.

 

The selection itself is worth a tour of the site and of this initiative.

 

Check: http://www.informationisbeautifulawards.com/

 

Longlist selection: http://www.informationisbeautifulawards.com/2012/07/our-longlist/

 

Shortlist selection: http://www.informationisbeautifulawards.com/2012/08/awardshortlist/

 

 


Via Robin Good, Dr. Stefan Gruenwald
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11 most read Machine Learning articles from in 2017

11 most read Machine Learning articles from  in 2017 | Best | Scoop.it

This article contains all the best articles of 2017 which gathered the interest of the Machine Learning community. If you wish to include any other learning resource/article here, please mention them in the comments.

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Best 7 FTP Clients for WordPress

Best 7 FTP Clients for WordPress | Best | Scoop.it

With so many FTP clients now available, which is the best option for WordPress users, and beginners alike? What is an FTP client and why do you need it? We’ve created a guide just for you so that you can choose the most secure, reliable, and best FTP tool for your WordPress website files (and understand what it all means)! What is an FTP client? File Transfer Protocol (FTP) is primarily used to transfer files (images, text, etc.) from one location to another. This is usually between a client (you) and a server (your web host).

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Learnable Programming: Designing a programming system for understanding programs

Learnable Programming: Designing a programming system for understanding programs | Best | Scoop.it

Khan Academy recently launched an online environment for learning to program. It offers a set of tutorials based on the JavaScript and Processing languages, and features a "live coding" environment, where the program's output updates as the programmer types.

 

  • Programming is a way of thinking, not a rote skill. Learning about "for" loops is not learning to program, any more than learning about pencils is learning to draw.
  • People understand what they can see. If a programmer cannot see what a program is doing, she can't understand it.

 

Thus, the goals of a programming system should be:

  • to support and encourage powerful ways of thinking
  • to enable programmers to see and understand the execution of their programs

 

A live-coding Processing environment addresses neither of these goals. JavaScript and Processing are poorly-designed languages that support weak ways of thinking, and ignore decades of learning about learning. And live coding, as a standalone feature, misses the point.

 

Alan Perlis once wrote, "To understand a program, you must become both the machine and the program." This view is a mistake, and it is this widespread and virulent mistake that keeps programming a difficult and obscure art. A person is not a machine, and should not be forced to think like one.

 

How do we get people to understand programming?

We change programming. We turn it into something that's understandable by people.

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10 Useful Python Data Visualization Libraries for Any Discipline

10 Useful Python Data Visualization Libraries for Any Discipline | Best | Scoop.it
While many Python data visualizations libraries are narrowly focused on accomplishing a certain task, these libraries can be used regardless of your field.

Via Carlos Lizarraga Celaya
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