In this live stream, i'll build a real-time translation app from scratch using Node.JS and Tensorflow.js. We'll learn how machine learning can be used to help translate languages theoretically and programmatically. We'll also learn about how Node Tensorflow work together and what the modern web development workflow that includes machine learning looks like. That's what keeps me going. Sign up for the next course at The School of AI: https://www.theschool.ai

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.

"It's very easy to get intimidated," says Hamayal Choudhry, the robotics engineer who co-created the smartARM, a robotic hand prosthetic that uses a camera to analyze and manipulate objects. "You have this idea for a project, then think, I don't know a thing about this." Here's how Choudhry and his partner Samin Khan, who programmed the smartARM's machine learning algorithm, used code libraries, college assignments, and sponsored hackathons to find and execute a meaningful project at age 20. The smartARM works by integrating the two fields of machine learning and mechatronics (robotics). A camera in the palm detects objects, and an algorithm analyzes the video feed (this is called computer vision) and tells the robotic hand how to manipulate the objects.

Sure you must have read my last article on which I treated how blockchain integration to Internet of Things will open the way to a new smart world. Now, let's discuss what the intersection of blockchain and the trending Artificial Intelligence could mean. For space and time constraint, I will refer you to read more on Blockchain technology. Apparently, the word Artificial means something not natural, a man-made entity or item. For ages now, technology has kept on feeding us with interestingly awesome innovations classified under this term.

The world around us is getting "smarter." Artificial intelligence (AI), data, and natural language processing have enabled Alexa, Siri, Pandora, Netflix, Facebook, Google, Amazon, Waze, and other platforms to become part of our lives. Both AI and robotics are projected to have a massive impact on the global economy. While anticipated improvements in GDP and efficiency are positive, some fear that jobs will be lost through automation. What will AI and robotics mean for higher education?

A new artificial intelligence approach can design drugs from scratch and is expected to help bring new drugs to patients quicker. Researchers from the University of North Carolina's (UNC) Eshelman School of Pharmacy have developed a new artificial intelligence system that can teach itself to create new drug candidates from scratch. Called Reinforcement Learning for Structural Evolution (ReLeaSE), the new approach is expected to potentially expedite the design and development of new drugs. The algorithm and computer programme features two neural networks, one of which carries knowledge on chemical structures for nearly 1.7 million biologically active molecules. The second network learns from the first one, over time, and proposes molecules with potential as new therapeutics.

Note: The popularity of this post has inspired me to write a machine learning test library. Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code.

Mining heavyweights BHP, Roy Hill and Alcoa have joined forces with universities and the CSIRO to develop a new data science training centre to upskill workers in the local resources sector. The $3.9 million Training Centre for Transforming Maintenance being funded by the Australian Research Council will be based at Curtin University and will be supported by Adelaide and Western Australia universities. Federal Education and Training Minister Senator Simon Birmingham says the new centre will equip engineers and graduates with data analytics skills to better manage the vast and expensive array of assets owned by mining and energy operators around the country. The centre's director, professor Andrew Rohl, said the effective maintenance of those assets is essential for the $205 billion annual output of the Australian resources sector. "However, maintenance management practices have changed little in the last 20 years and are ripe for a digital overhaul that will bring developments in computational methods, statistics, applied mathematics and artificial intelligence to determine how, when and why maintenance is conducted," said Rohl. "The new centre, which will bring together the relevant research and industry expertise, will enable the development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets."

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.

Long before NBC's The Office made Scranton a lovable character, the historic city in Pennsylvania's northeast corner has been an important voice in the nation's narrative. With roots as an anthracite coal king, the city continues to lead the Commonwealth with innovative programs in the arts, culture, science, and healthcare. The University of Scranton is one of the city's educational leaders that honors its prestigious legacy while keeping an eye to the future, and collaborative technologies help set the institution apart. As the media and control system engineer of The University of Scranton, Robert Kennedy, CTS designs the touchpanels of the school's control systems and supports audiovisual technology in nearly 200 classrooms. He also works on AV system implementation, collaborating with outside integrators when necessary.