programming


The best robotics kits for beginners

Engadget

This post was done in partnership with Wirecutter, reviews for the real world. When readers choose to buy Wirecutter's independently chosen editorial picks, it may earn affiliate commissions that support its work. After spending 35 hours researching and testing seven of the best kits for learning robotics, we found the Lego Boost to be the best kit for most beginners. With its Lego-based design, built-in sensors, and the most expansive set of options for creativity and personalization, it was the most fun to build with. And the streamlined tablet app's user-friendly instructions and super-simple programming made it the easiest to learn of any of the kits we tried.


Confused by data visualization? Here's how to cope in a world of many features - Dataconomy

@machinelearnbot

Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data. Now more than ever, data are collected from every aspect of our lives. From social media and advertising to artificial intelligence and automated systems, understanding and parsing information have become highly valuable skills. But we often overlook the importance of knowing how to communicate data to peers and to the public in an effective, meaningful way. The first tools that come to mind in considering how to best communicate data – especially statistics – are graphs and scatter plots.


Google wants to solve new AI problems: Jeffrey Dean

#artificialintelligence

Tokyo: Jeffrey (Jeff) Dean is a Google senior fellow in a research group at Google where he leads the company's artificial intelligence (AI) project called Google Brain. Along with his team, Dean, who joined Google in 1999, is currently implementing the company's vision as articulated by chief executive Sundar Pichai--to build an "AI-first" world. In an interview on the sidelines of a "Google #MadewithAI" event, held recently in Tokyo, Dean explains what this vision encompasses and the challenges involved in implementing it. What are the major steps involved in this process of implementing the Google strategy of building an AI-first world? The steps involve making products that are useful, help others innovate and solve humanity's big challenges.


Online Master of Science in Business Analytics - Business Analytics @ Tepper

@machinelearnbot

The Tepper School of Business developed the curriculum for the online Master of Science in Business Analytics (MSBA) program from the ground up with this question in mind. In consultation with global business leaders, they determined that the greatest need is for professionals who not only have advanced analytical skills, such as machine learning and optimization, but also the appropriate business knowledge and communication skills to solve complex problems and bring value to industry. Our students develop proficiency in the full range of state-of-the-art business analytics techniques; they also learn how to tell stories through and extract insights from data. Given the Tepper School's view of a curriculum as an organic entity, our faculty continually work in concert to ensure that courses harmonize, even as they are individually updated and modified to ensure learning outcomes for students are always in step with an ever-evolving industry. The flexible online format enables students to continue working while earning their degree and apply what they learn in the classroom to their work environment.


Traffic data is abundant, Techies find ways to make it both valuable and fun - Mobility Lab

@machinelearnbot

Traffic experts met last week at Spaces NoMA for the fourth Playing with Traffic event of Transportation Techies. A handful presented their latest work in a rapid-fire show-and-tell of the wide array of open-source mapping and imaging that can now inform how streets are planned for both current users and future technology. Mapillary's Janine Yoong explained how combining computer vision – using digital images to train computers to understand objects – with human collaboration can inform the development of autonomous vehicles. Yoong and her team hope to use street-view images from across the internet to help driverless cars better categorize items that they "see" while also creating fresher, more accurate, and complete maps that can help computers understand their location. With this, Mapillary pulls images of streetscapes from around the world, including remote arctic research bases, that can train AV programs by processing as many objects and situations as possible.


Python, Data Science and Tech Culture at PyCon 2017

#artificialintelligence

Tech, software development and Data Science is currently thriving in Dublin. To meet the demands of that crowd, PyConIE, Ireland's largest indigenous software development conference, was run late October from the Radisson Blue hotel, Dublin. With speakers and attendees flying in from all over the world, the PyConIE conference got to see some big names and share that knowledge. PyConIE has been running in Dublin for eight years now, and 2017 was the biggest year yet, with over 40 speakers, nearly 400 attendees, two tracks and two workshops covering everything from Data Science to developing in Python, to tools and even to tech culture. Speakers were represented from around the world, and from Ireland's tech scene – where there were speakers from the Dublin tech scene, and even winners from the DatSci awards.


Connecting R to Keras and TensorFlow

@machinelearnbot

TensorFlow itself is implemented as a Data Flow Language on a directed graph. Operations are implemented as nodes on the graph and the data, multi-dimensional arrays called "tensors", flow over the graph as directed by control signals. This kind of programming will probably strike most R users as being exotic and obscure, but my guess is that because of the long history of dataflow programming and parallel computing, it was an obvious choice for the Google computer scientists who were tasked to develop a platform flexible enough to implement arbitrary algorithms, work with extremely large data sets, and be easily implementable on any kind of distributed hardware including GPUs, CPUs, and mobile devices. The TensorFlow operations are written in C, CUDA, Eigen, and other low-level languages optimized for different operation. Users don't directly program TensorFlow at this level.


Artificial Intelligence Programming in Java

#artificialintelligence

There is a list of programming languages are available for developing an artificial intelligence project such as Python, POP-11, C, MATLAB, Java, Lisp, and Wolfram language. In this article, you find How Java programming works with Artificial Intelligence. The main feature of Java is Java virtual machine. Java virtual machine is an abstract machine and is available in many hardware and software platform. Java virtual machine performs an operation like loads code, verifies code, provide a runtime environment, and executes code.


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Mashable

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Tertulias: Talking heads on Spain's airwaves

Al Jazeera

Television programming in Spain has undergone a transformation over the past decade - changes driven partly by economics and partly by politics. Ever since the banking crisis of 2008, the country has been in a semi-constant state of political upheaval. A series of corruption scandals, inconclusive general elections and, more recently, Catalonia's run at independence have kept Spaniards glued to their televisions and pundits talking 24/7. That has given rise to a wave of political talk shows that the Spanish call tertulias. These programmes meet two important criteria, they provide political flashpoints that audiences seem to like and they're cheap to produce.