If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Well folks, I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.

In a blog post, Coursera co-founder Daphne Koller announced she is leaving the company to join Alphabet subsidiary Calico. Koller founded Coursera with Andrew Ng back in 2012 after working together on artificial intelligence research at Stanford. The two supported the company's growth until Ng left to become chief scientist at Baidu's research arm. Today Coursera has grown to tech 20 million students material from 1,300 courses. Though both have now officially moved on to new challenges, Koller and Ng remain co-chair's of the Coursera Board of Directors.

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school.

We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner.

At Harvard Business School (HBS), MBA students are pondering a future when robots rule the road. The pioneers of the driverless car movement -- such as Google and Tesla -- are mapping the MBAs a future in which artificial intelligence and robotics will likely impact the entire job market and global economy. David Yoffie, professor of international business administration at HBS, believes such disruptive technologies are now an "essential" part of the b-school landscape. "What I'm trying to teach students is: What can these technologies deliver? And what are the challenges and opportunities for a company that does AI?" he says. David's offered his MBAs two cases on artificial intelligence (or AI) and deep learning, and reckons that many of his colleagues at HBS are bringing robots into the curriculum too: "It's a capability that MBAs need to know about," he says.

Coding will join this list in time, however, where it differs wildly from the afore mentioned examples is it is unlikely to be lovingly preserved for future generations to admire, fiddle with or better still, reactivate. Its essence will not be reified for one specific reason – it can't be touched and humans value tactility. We touch immediately, both inside and outside the womb. Today, we find ourselves at a pivotal moment in our existence and about to experience an exponential period of rapid technological growth the likes of which is quite probably beyond our comprehension and at a base level, will have serious implications for coding. We rather arrogantly think that because we have a good grasp of our own technological advancement so far, we can somehow predict the mass cultural and behavioural shift about to happen as we question our own skills in the world.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.

During the last two years, Microsoft has upped the ante on Machine Learning and Analytics. From hiring top notch data scientists to acquiring niche startups, Redmond has made the all the right moves to transform Azure into one of the best analytics platforms. These investments have started to pay off for the company. It has been successful in articulating and demonstrating the value of data-driven insights to governments, medical institutions, and public sector organizations. Emerging markets that are turning technology savvy are becoming the hotbed for evaluating the upcoming trends such as Machine Learning and Artificial Intelligence.

Robots have come a long way from being depicted as fictional characters in movies to playing an important role in executing critical business tasks, thanks to a versatile technique called'deep learning'. However, there is a growing concern that robots are expected to soon replace humans at operational level in organizations. With prominent leaders openly talking about how people will soon lose jobs to high functioning machines and companies stringently reducing employee strength, there is no denying the fact that artificial intelligence (AI) technology is here to stay. If one were to take a harder look however, one can say that on the contrary AI presents opportunities for the existing workforce to work in concert with the machines. Businesses should be on top of their game in making the best use of AI.

A summer program at Stanford University introduced high school girls to artificial intelligence this summer. Among the activities they learned more about were flying drones, how autonomous cars work, diving robots and machine learning for healthcare. The two-week AI program was developed last year by Olga Russakovsky, a Stanford postdoctoral researcher, and Fei-Fei Li, associate professor of computer science and director of Stanford's AI Lab. They were motivated by a "desperate" need to bring more women into the field. As Li told the girls during their first day, as explained in a blog entry, AI could in the future become the "Terminator next door," or follow a more humane direction, based on the people behind the scenes doing the research and development work.