Alright, enough talking, let's see some code! Install Jupyter - There are many ways to install Jupyter, but the easiest way is to download and install Anaconda. Install dotnet try - The C# kernel is based on the dotnet try tool. Install the .NET Jupyter Kernel – to connect Jupyter with the dotnet try tool, execute the following command in a command prompt or PowerShell to install the .NET Kernel: dotnet try jupyter install To start Jupter Notebooks, open Anaconda and click on Jupyter Notebook. It's always easier to learn something new using examples.

AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react to situations you never anticipated. Which programming language should you learn to plumb the depths of AI? You'll want a language with many good machine learning and deep learning libraries, of course. It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages.

That is, the jobs that are in the greatest danger of being disrupted, if not altogether displaced, by machines are occupied by blue-collar and front-line service workers--those in "lower- wage, lower-education roles" who perform rote tasks, as a report from the Brookings Institution framed it earlier this year. But a new study from Brookings, being released today, challenges this assumption, at least as it pertains to artificial intelligence. "White-collar, well-paid America--radiologists, legal professionals, optometrists, and many more--will likely get no free pass," it asserts. In fact, Brookings says, "better-educated, better-paid workers will be the most affected" by AI. This modified view is based on a novel research technique developed by a Stanford PhD student in economics named Michael Webb, who built his own algorithm to compare language from 16,400 AI patents with the specific words used to describe 769 different jobs in the government's official occupational database, known as O*NET.

One afternoon last month, as I was crossing a busy four-lane street that runs through the University of Pittsburgh campus, I looked up to see a robot blocking my path. Over the summer, several four-wheeled, knee-high robots had been roaming campus, unmarked and usually with a human handler several feet behind. Recently they'd multiplied, and now they were flying solo. They belonged to Starship Technologies, I learned, an autonomous delivery service rolling out on college campuses across America. As a chemical engineering Ph.D. student at the University of Pittsburgh who uses a power wheelchair, I figured it wouldn't be long before I met one of these bots in a frustrating face-off on a narrow sidewalk.

Facebook recently announced the release of PyTorch 1.3. The latest version of the open-source deep learning framework includes new tools for mobile, quantization, privacy, and transparency. Engineering director Lin Qiao took the stage at the recent PyTorch Developer Conference in San Francisco to highlight new features in the release, framing them with PyTorch's core principles of developer efficiency and building for scale. For building at scale, the release introduces new model quantization capabilities as well as support for mobile platforms and tensor-processing units (TPUs). Developer efficiency tools include tools for model transparency and data privacy.

The latest generation of Intel VPUs includes 16 powerful processing cores (called SHAVE cores) and a dedicated deep neural network hardware accelerator for high-performance vision and AI inference applications--all at low power.

The technology industry and policymakers need to address public concerns about artificial intelligence (AI) which are "not the fault of AI" itself, a tech executive said Tuesday. "It is the fault of developers, so we need to solve this problem," said Song Zhang, managing director for China at global software consultancy, ThoughtWorks. Consumer worries relating to AI include concerns about personal privacy and how the systems may get out of control, said Zhang during a panel discussion discussing the "Future of AI" at CNBC's East Tech West conference in the Nansha district of Guangzhou, China. It is the duty of the tech industry and policymakers to focus on, discuss and solve such problems, said Zhang in Mandarin, according to a CNBC translation. Indeed, while consumers are curious about AI when they first come into contact with the technology, their mindset changes over time, said Rong Luo, chief financial officer of TAL Education Group.

In today's factories and warehouses, it's not uncommon to see robots whizzing about, shuttling items or tools from one station to another. For the most part, robots navigate pretty easily across open layouts. But they have a much harder time winding through narrow spaces to carry out tasks such as reaching for a product at the back of a cluttered shelf, or snaking around a car's engine parts to unscrew an oil cap. Now MIT engineers have developed a robot designed to extend a chain-like appendage flexible enough to twist and turn in any necessary configuration, yet rigid enough to support heavy loads or apply torque to assemble parts in tight spaces. When the task is complete, the robot can retract the appendage and extend it again, at a different length and shape, to suit the next task.

Europe is focussed on making robots that work for the benefit of society. This requires empowering future roboticists and users of all ages and backgrounds. In its 9th edition, the European Robotics Week (#ERW2019) is expected to host more than 1000 events across Europe. Over the years, and over 5,000 events, the organisers have learned a thing or two about reaching the public, and ultimately making the robots people want. For many, robots are only seen in the media or science fiction.

Data visualization: In this section, you will learn how to create simple plots like scatter plot histogram bar, etc. Data manipulation: You will learn in detail about data manipulation. GUI Programming: This section is a combination of life instructor-led training and self-paced learning. Developing web Maps and representing information using plots: In this section, you will understand how to design Python applications. Computer vision using open CV and visualization using bokeh: You will also learn designing Python application in the section.