Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

We have been talking about the cybersecurity skills gap for a decade, but the pain is truly being felt now as businesses churn out new apps with insufficient security, says WhiteHat Security's Craig Hinkley. What is machine learning's role? See Also: Live Webinar Benchmarking Your Organization's Security Performance with Security Ratings Hinkley joined WhiteHat Security as CEO in early 2015, bringing more than 20 years of executive leadership in the technology sector to this role.

In a first for machine-learning algorithms, a new piece of software developed at Caltech can predict behavior of bacteria by reading the content of a gene. The breakthrough could have significant implications for our understanding of bacterial biochemistry and for the development of new medications. One thrust of modern pharmacology is focused on alleviating ailments by developing drugs that target specific proteins that reside in the membranes of our bodies' cells. These proteins, known as integral membrane proteins (IMP), act as receptors or "gates" that allow materials into and out of cells. Examples of IMPs are G-protein-coupled receptors, which relay information to a cell about its environment, and ion channels, which control the interior environment of a cell by acting as gatekeepers that selectively allow ions to pass in and out of the cell. IMPs are the targets of nearly 50 percent of all drugs on the market.

Late on the night of October 4, 1957, Communist Party Secretary Nikita Khrushchev was at a reception at the Mariinsky Palace, in Kiev, Ukraine, when an aide called him to the telephone. The Soviet leader was gone a few minutes. When he reappeared at the reception, his son Sergei later recalled, Khrushchev's face shone with triumph. "I can tell you some very pleasant and important news," he told the assembled bureaucrats. "A little while ago, an artificial satellite of the Earth was launched."

Govans Elementary School Principal Linda Taylor says low access to computers makes it difficult for students to develop computer skills. Taylor tells The Baltimore Sun that some students struggled to use a mouse during standardized tests last year. The Sun reports researchers have found that low-income households across the nation continue to lag in technology adoption.

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go).

Joanna Goodman is a freelance journalist and author. She is The Law Society Gazette's IT columnist and writes about tech for other publications including The Guardian. Her focus is emerging tech, including artificial intelligence (AI), connected devices and robots. She is a visiting scholar at University of Westminster Law School. As she is also the author of ones of the very first books on AI in the legal sector, we were fascinated by her views on just how worried lawyers should be when it comes to the impact of these technologies.

You may even call it post-doctorship, as the level is beyond the traditional PhD degree. It is not a degree, not competing with university programs, but instead, akin to a fellowship or apprenticeship to learn doing state-of-the-art applied research, discover ground-breaking results or applications, and translate your discoveries into seminal material suitable for a broad audience. It is intended for professionals with substantial experience, perhaps to people who already have a PhD in a different field. It is mentored by well connected, world-class recognized scientists (not necessarily affiliated with a university) with broad domain of expertise in many environments. The focus is on real-world problems and applications to help you get a high-level position in the industry or as an independent researcher.

Everyone in, and serving, manufacturing businesses has encountered terms such as Industry 4.0, Digital Transformation, and Smart Factory. Small compute footprints, low-cost, high-availability communication systems, high-capacity–low-cost memory, rapidly evolving sensor technology, and new time-series data structures supporting real-time analytics are all conspiring to empower industrial operators to transform their businesses from "tribal knowledge systems" to data-driven operations. Twenty years ago -- in the dawn of the Internet age, at a converting operation then part of International Paper, we relied on Jake. Jake was a medium age, good natured man who had worked the plant for seventeen years -- since high school. Jake, unusual in that he was able to stand the middle ground between Union and Management, epitomized "tribal knowledge."

The following is a list of free books on Machine Learning. A Brief Introduction To Neural Networks provides a comprehensive overview of the subject of neural networks and is divided into 4 parts –Part I: From Biology to Formalization -- Motivation, Philosophy, History and Realization of Neural Models,Part II: Supervised learning Network Paradigms, Part III: Unsupervised learning Network Paradigms and Part IV: Excursi, Appendices and Registers. A Course In Machine Learning is designed to provide a gentle and pedagogically organized introduction to the field and provide a view of machine learning that focuses on ideas and models, not on math. The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. An undergraduate in their fourth or fifth semester should be fully capable of understanding this material. However, it should also be suitable for first year graduate students, perhaps at a slightly faster pace.