Last year, I started writing about my experiences taking courses on machine learning and artificial intelligence. One of the big, unexpected problems I ran into was calculus and linear algebra. I've found that many online courses say you don't need much mathematics fundamentals to be a programmer, but inevitably, even in beginner courses, the underlying math was important to understand what was going on. The need for remedial math seems widespread enough that even a simple Google search for'calculus and artificial intelligence' turns up a bunch of blogs and additional courses on how to understand the math underlying these assignments. After spending a lot of time online trying to sort through this haystack of do-it-yourself calculus blogs, college class PDFs, and other resources, I came away with two websites that were outstanding for teaching basic calculus and linear algebra: Khan Academy and an on-demand tutoring service called Yup.

A monitor is a concurrent programming language construct invented by Hoare15 and Brinch Hansen.11 A monitor is an object with a built-in mutex lock, which is acquired automatically when a method is called and released when the method returns.

In this talk you will learn what you need to know in order to build intelligent Cloud solutions with power of AI and Machine Learning. I will show you how to build a web app that can search through text, documents and images, and insights on Azure Search and Cognitive Services that enable to build such solutions.

The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 60 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year. The quality of research and education in both HICT universities is world-class, and the education is practically free as there are no tuition fees for doctoral students in the Finnish university system. In terms of the living environment, Helsinki has been ranked as one of the world's top-10 most livable cities (Economist, 2017), and Finland is among the best countries in the world with respect to many quality of life indicators, including being the overall #1 country in human wellbeing. Helsinki is in the second place in the world's startup city comparison (Valuer, 2018) and is also the Mobile Data Capital of the World (IEEE Spectrum, 2018). The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple interesting research projects to choose from. If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions (listed below).

Is voice technology going to become the next big opportunity for brands to engage with consumers? As we enter an era where voice assistants are becoming more popular (Amazon Alexa, Google Home, Apple HomePod, among others), new doors open for marketing and communications professionals. Experts say voice assistants can become the first interactive tool at home that provides brands the capability to dynamically offer up ads in the future that could be user controlled. In this interactive Master Class, we will understand voice technology as part of artificial intelligence, discuss real business examples, and conclude with a discussion on the future of this technology and the marketing field as we know it.

The demand for data science and machine learning jobs is rapidly rising and the gap between this demand and the number of data scientists available is still very wide. Now, people from an engineering background and pure sciences are shifting their careers in the field of ML. Physics is one such background which falls into this category because of the high level of logic and mathematics required in an ML job. Physics research requires dealing with a lot of data, just like ML. Physicists are also proficient in at least one programming language -- most likely Python, as it is popular in the Physics community as well. There are many physicists today who are data scientists.

Before they got down to business for the day, students in Devin Tatro's social studies class were offered a quiet moment of self-reflection: On this golden fall afternoon at Nome-Beltz Junior/Senior High School, were they feeling chipper, distressed, or somewhere in between? One by one, they selected the picture of the facial expression that best matched their mood, and with a swift click sent an answer to the teacher. She scanned the responses and made a few mental notes. Then, without missing a beat, she switched the smartboard display and launched into a multiple-choice quiz using a game-based online learning platform called Kahoot! "Tell me one thing you remember about yesterday's lesson on expansions and tax on Native Americans," Tatro said, pacing the front of the classroom. She rattled off students' responses as they popped up on the smartboard in a colorful word cloud: "Forced relocation, reduced population, disease, warfare, cultural destruction ... wow, that's a powerful term."

Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. Choosing the learning rate is challenging as a value too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process. The learning rate may be the most important hyperparameter when configuring your neural network. Therefore it is vital to know how to investigate the effects of the learning rate on model performance and to build an intuition about the dynamics of the learning rate on model behavior. In this tutorial, you will discover the effects of the learning rate, learning rate schedules, and adaptive learning rates on model performance. Deep learning neural networks are trained using the stochastic gradient descent algorithm. Stochastic gradient descent is an optimization algorithm that estimates the error gradient for the current state of the model using examples from the training dataset, then updates the weights of the model using the back-propagation of errors algorithm, referred to as simply backpropagation.

When you start work as a data scientist you begin to realize that there is a huge gap between the models you've learned to the real life applications. You acknowledge then, that what makes someone professional is the ability to deliver data science application in a short time frame. And this is what this course is all about. How to create, quickly, applications which based on predictive modeling. The materials in this course are not things I read in books, but lessons learned in the hard way, after many years of building predictive applications.

TL;DR: I had a pleasure to be a part of the first AI conference in Somaliland, organised by Shaqodoon, HarHub and Elmi Academy, and featuring speakers from Google, MIT, major Somaliland telecoms, banks, University of Hargeisa and Ministry of Telecommunication & Technology of Somaliland. See slides (lectures workshops) and event program for details. Below are my personal notes and pictures from this trip. Whenever I tell this story people seem to be surprised with choice of spending vacation time in Somaliland and running an AI-related event there. So let me share some first-hand experience with you and explain why trips and events like this are useful, fun and safe.