It covers self-study tutorials and end-to-end projects on topics like: Loading data, visualization, modeling, algorithm tuning, and much more...

Project 1: Movie Recommendations For this project, you'll receive provide data about movies and users which will be used to train the model and generate recommendations for users about which movies they would like to watch, using the collaborative filtering technique. The data set for this project contains information on customers who received a Home Equity Line of Credit. The target variable is a flag. If the value is a "1" then the person defaulted on the loan. If the value is a "0" then the person repaid the loan.

Stochastic gradient descent (SGD) is commonly used for optimization in large-scale machine learning problems. Langford et al. (2009) introduce a sparse online learning method to induce sparsity via truncated gradient. With high-dimensional sparse data, however, the method suffers from slow convergence and high variance due to the heterogeneity in feature sparsity. To mitigate this issue, we introduce a stabilized truncated stochastic gradient descent algorithm. We employ a soft-thresholding scheme on the weight vector where the imposed shrinkage is adaptive to the amount of information available in each feature. The variability in the resulted sparse weight vector is further controlled by stability selection integrated with the informative truncation. To facilitate better convergence, we adopt an annealing strategy on the truncation rate, which leads to a balanced trade-off between exploration and exploitation in learning a sparse weight vector. Numerical experiments show that our algorithm compares favorably with the original algorithm in terms of prediction accuracy, achieved sparsity and stability.

With Artificial Intelligence (AI) now being seen as an essential tool in various sectors, it is important for our generation to incorporate innovative dynamic learning needs into our global education system. But when cutting-edge sectors evolve at lightning pace, it is not always possible for traditional sectors to change at the same speed. My company, Gravity4, has been intensively exploring Deep Learning in our development lab to advance our understanding with the ad technology platforms. Recently, I did a mentorship series with the youth in a struggling education system. The fascination of all great things possible, through the cutting edge revolution of AI, bought much energy in the room.

Artificial intelligence (AI) systems can recognize your speech like Siri or identify images like Facebook, but these types of machine intelligences are built on statistical approximation, using loads of data to make educated guesses. Though statistical approximation was a significant technological advancement for devices, experts at Future Lab's AI Summit in New York City believe that it is time to expand the bounds of artificial intelligence--to democratize it--by "engineering knowledge." For Puri, that is the next level of AI--its ability to not only say what something is, but to reason and understand the intent of its being, to answer the'why' question. "Working with kids gives you grounding. They ask questions because they are not shy," says IBM Watson's Chief Architect, Dr. Ruchir Puri, in an interview with EdSurge.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well. The primary motivation and driving force for these areas of study, and for developing these techniques further, is that the solutions required to solve certain problems are incredibly complicated, not well understood, nor easy to determine manually.

A year ago the University of California at Berkeley hosted a retirement celebration for David Patterson, who was hanging it up after a 40-year academic career in computer architecture. Patterson encored the event last May with a personal 16-minute history, chronicling his days as a wrestler in high school and college and a math major at UCLA, followed by a job at Hughes Aircraft and four decades at Berkeley. From writing two books with Stanford University's John Hennessy to chairing the Computing Research Association, Patterson told the audience that a key to his success was doing "one big thing at a time." Rather than hitting the beach after retirement, Patterson joined Google in July to work on an ambitious new chip that's designed to run at least 10 times faster than today's processors and is sophisticated enough to handle the intensive computations required for artificial intelligence. It's called the Tensor Processing Unit (TPU), and Patterson has emerged as one of the principal evangelists. He spoke to about 100 students and faculty members at the Berkeley campus on Wednesday, a few days shy of the anniversary of his retirement celebration.

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in five departments at California State University Northridge (CSUN), based on a minimal number of lower division courses in each major. In addition, we use the detected clusters to identify hidden bottleneck courses.

Disclaimer: I am an IT guy and my knowledge on human body is limited to my daughter's high school biology class book and information obtained from search engines. So, excuse me if any of the information below is not represented accurately!! Human body is the most complex machine ever created. With a complex network of interconnected organs, millions of cells and the most advanced processor, human body is the most automated system in this planet. In this article, we will draw comparisons between working of a human body to that of a data center. We will draw parallels between human body automation to data center automation and explain different levels of automation we need to drive in data centers.