In one back room at Skyline High School, you can learn all you need to know about St. Vrain Valley School District. It's there that bins of materials sit next to past projects, exposing the district's DNA. Boxes holding glue, Popsicle sticks, tape, pipe cleaners, compasses, zip ties and rulers lie nestled inside a 6-foot-high, student-constructed rack. Behind the storage unit sits a rectangular wooden box stuffed with bicycle tires filled with Silly Putty to replicate human intestines. For that biomedical project, students had to create a probe and learn to maneuver it sight unseen from behind a curtain on the box's opening to procure a sample from the intestine/bike tire.

Follow leaders in ML on twitter to see what research papers/blog posts/etc. This is a very effective but highly under-rated way to get good at ML. Having seen a lot of new Stanford PhD students grow to become great researchers, I can say confidently that replicating others' results (not just reading the papers) is one of the most effective ways to see and make sure you understand the details of the latest algorithms. Many people jump too quickly into trying to invent something new, which is also worth doing, but is actually a slower way to learn and build up your foundation of knowledge. When you do build something new, publish it in a paper or blog post and consider open-sourcing your code, and share it back out with the community! Hopefully this will help you get more feedback from the community, and further accelerate your learning.

We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed bandit problems, the manager needs to simultaneously "explore" and "exploit" with her inventory decisions, in order to minimize the cumulative cost. We make no probabilistic assumptions---importantly, independence or time stationarity---regarding the mechanism that creates the demand sequence. Our goal is to shed light on the hardness of the problem, and to develop policies that perform well with respect to the regret criterion, that is, the difference between the cumulative cost of a policy and that of the best fixed action/static inventory decision in hindsight, uniformly over all feasible demand sequences. We show that a simple randomized policy, termed the Exponentially Weighted Forecaster, combined with a carefully designed cost estimator, achieves optimal scaling of the expected regret (up to logarithmic factors) with respect to all three key primitives: the number of time periods, the number of inventory decisions available, and the demand support. Through this result, we derive an important insight: the benefit from "information stalking" as well as the cost of censoring are both negligible in this dynamic learning problem, at least with respect to the regret criterion. Furthermore, we modify the proposed policy in order to perform well in terms of the tracking regret, that is, using as benchmark the best sequence of inventory decisions that switches a limited number of times. Numerical experiments suggest that the proposed approach outperforms existing ones (that are tailored to, or facilitated by, time stationarity) on nonstationary demand models. Finally, we extend the proposed approach and its analysis to a "combinatorial" version of the repeated newsvendor problem.

Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable quantum transverse field to generate tunneling processes. A key challenge is to identify classes of non-convex optimization problems for which quantum annealing remains efficient while thermal annealing fails. We show that this happens for a wide class of problems which are central to machine learning. Their energy landscapes is dominated by local minima that cause exponential slow down of classical thermal annealers while simulated quantum annealing converges efficiently to rare dense regions of optimal solutions.

I started my deep learning journey a few years back. I have learnt a lot in this period. But, even after all these efforts, every Neural network I train provides me with a new experience. If you have tried to train a neural network, you must know my plight! But, through all this time, I have now made a workflow, which I will share with you today.

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too! If you thought learning data science is difficult or deep neural nets is not your cup of tea – look up to the role models who created them. Following these role models provides you a daily inspiration, a motivation to find bigger purpose in life and to achieve it. Role models set goals for you and try to make you as good as they are. In this article, I'll introduce you to a league of ultimate data scientists in the world.

After factoring job satisfaction, salary, and openings, Glassdoor ranked Data Science careers #1 (with a Job Score of 4.8/5) and determined the average salary of a Data Scientist to be $118,709. Moreover, McKinsey estimates that market demand for such talent will dramatically outpace the supply – leaving as many as 190,000 unfilled positions in 2018 (in the U.S. alone). Companies are scrambling to find those rare individuals with the ability to synthesize complex math, computer science, engineering, and creativity. Do a quick search of any job board, and you will see such positions posted (and re-posted) daily. INSOFE – a globally-recognized Data Science institute established to cultivate quantitative and Machine Learning skills, in tandem with Soothsayer Analytics – a US-based Data Science & Artificial Intelligence firm, are jointly offering an innovative educational experience that synthesizes world-class education with real-world experience.

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. You can apply to the degree program either before or after you begin the Specialization.

In the last two years, artificial intelligence (AI) has started to become a mainstream topic, but core aspects of AI, like machine learning and data science, have been around for quite some time, so the technology has had plenty of time to impact the industry already. According to a study by Tata Consultancy Services, more than 90% of companies in the energy, high tech, telecom, retail, and automotive industries use AI today. The company researched 13 industries globally and found that more than 80% of companies use AI in some capacity. "Beyond the IT function, artificial intelligence is most often used in customer service, sales, marketing, and finance," states the report. "In energy, 100% of companies use AI – the only industry in which every company is using it. Of the companies that don't use AI today, all expect to by 2020."

For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher …