In 2012, Geoffrey Hinton changed the way machines see the world. Along with two graduate students at the University of Toronto, Mr. Hinton, a professor there, built a system that could analyze thousands of photos and teach itself to identify common objects like flowers and cars with an accuracy that didn't seem possible. He and his students soon moved to Google, and the mathematical technique that drove their system -- called a neural network -- spread across the tech world. This is how autonomous cars recognize things like street signs and pedestrians. But as Mr. Hinton himself points out, his idea has had its limits.

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.

My current research trajectory centers on what I call learning programming at scale. Decades of prior research have worked to improve how computer programming is taught in traditional K–12 and university classrooms, but the vast majority of people around the world--children in low-income areas, working adults with full-time jobs, the fast-growing population of older adults, and millions in developing countries--do not have access to high-quality classroom learning environments. Thus, the central question that drives my research is: How can we better understand the millions of people from diverse backgrounds who are now learning programming online and then design scalable software to support their learning goals? One critical prerequisite for improving how programming is taught is to understand why and how people are currently learning and what obstacles they face. To work toward this goal, I have been studying traditionally under-represented learner populations and non-traditional learning environments.

Perhaps you remember the iconic theme of the globally popular Kung Fu Panda movies, "You are the secret ingredient!" This meant that self-belief is important and with it great things can be achieved--Po, for example, became the Dragon Warrior. My meaning here is that computer science is both a powerful enabler of rapid advances in all intellectual fields and a disruptor driving furious revolutions in commerce and society worldwide. Computer science is more important and potent than ever! Computing is driving unprecedented rapid change.

Despite their popularity, the practical performance of asynchronous stochastic gradient descent methods (ASGD) for solving large scale machine learning problems are not as good as theoretical results indicate. We adopt and analyze a synchronous K-step averaging stochastic gradient descent algorithm which we call K-AVG. We establish the convergence results of K-AVG for nonconvex objectives and explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is a special case of K-AVG with $K=1$. We also show that K-AVG scales better than ASGD. Another advantage of K-AVG over ASGD is that it allows larger stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD implementations and achieves better accuracies and faster convergence for \cifar dataset.

Language-learning apps are nothing new, with offerings from MIT and Duolingo ready to teach you a new way to communicate right on your phone. Now Microsoft is looking to teach you Chinese with a free new AI-powered iOS app. The idea here is to provide users with a way to practice the Chinese language in the absence of real-life communicative partners. "You think you know Chinese, but if you meet a Chinese person and you want to speak Chinese, there is no way you can do it if you have not practiced," said Microsoft's Yan Xia in a blog post. There's no word on plans to expand to other languages, but it's not hard to see such an app helping you learn to converse in different tongues, too.

Did you know Python is the most popular language in the Data Science and Machine Learning Market? Easy and versatile, Python is a first step in many new age technologies like Machine Learning, Data Science, Deep Learning, and Artificial Intelligence. Our newly-launched Python Foundation Nanodegree would prepare with everything you need to become a Python expert.

Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

NEW YORK; Nov. 14, 2017 – Accenture (NYSE: ACN) has launched the Accenture Future Talent Platform, an interactive learning platform that includes talent transformation services to help clients develop their IT workforces in critical areas such as digital, cloud, security and artificial intelligence. Accenture has already used the platform internally to train more than 180,000 of its people globally in the latest digital technologies – or New IT – in just over 20 months. Now, through its technology consulting and talent and organization practices, Accenture is bringing these learning capabilities to clients to help them run agile, intelligent businesses. "With technology accelerating at a breathtaking pace, companies need to upskill their people at greater speed and scale to avoid being disrupted by competitors," said Bhaskar Ghosh, group chief executive of Accenture Technology Services. "Accomplishing this requires creating a culture of continuous learning that empowers people to build new skills. The Accenture Future Talent Platform helps companies do exactly that through a unique, interactive experience and rich curriculum."

I recently completed all available material (as of October 25, 2017) for Andrew Ng's new deep learning course on Coursera. I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner. For example, Ng makes it clear that supervised deep learning is nothing more than a multidimensional curve fitting procedure and that any other representational understandings, such as the common reference to the human biological nervous system, are loose at best. The specialization only requires basic linear algebra knowledge and basic programming knowledge in Python.