Regardless of the Financial Services sector - Trading, Asset Management, Banking, Wealth Management, and many others - have increased pressure for real-time analytics with complex connected data. The view of complex dependencies, historic data, risk and compliance has never been more important for all executives and financial practitioners alike. The focus of this webinar is to identify how Graph and Machine Learning can directly increase accuracy, avoid fines and compliance issues, and drive revenue. This event is designed as a'Speed Dating' format with focus on key topics for under 15 minutes in order to maximize the insights. During this online meet up, you'll learn from our experts on how Expero and TigerGraph technology can unlock the potential in your organization.

Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now playing a pivotal role in various aspect of society. The goal in statistical learning is to use data to obtain simple algorithms for predicting a random variable $Y$ from a correlated observation $X$. Since the dimension of $X$ is typically huge, computationally feasible solutions should summarize it into a lower-dimensional feature vector $T$, from which $Y$ is predicted. The algorithm will successfully make the prediction if $T$ is a good proxy of $Y$, despite the said dimensionality-reduction. A myriad of ML algorithms (mostly employing deep learning (DL)) for finding such representations $T$ based on real-world data are now available. While these methods are often effective in practice, their success is hindered by the lack of a comprehensive theory to explain it. The information bottleneck (IB) theory recently emerged as a bold information-theoretic paradigm for analyzing DL systems. Adopting mutual information as the figure of merit, it suggests that the best representation $T$ should be maximally informative about $Y$ while minimizing the mutual information with $X$. In this tutorial we survey the information-theoretic origins of this abstract principle, and its recent impact on DL. For the latter, we cover implications of the IB problem on DL theory, as well as practical algorithms inspired by it. Our goal is to provide a unified and cohesive description. A clear view of current knowledge is particularly important for further leveraging IB and other information-theoretic ideas to study DL models.

Intelligent tutoring systems have been shown to be effective in helping to teach certain subjects, such as algebra or grammar, but creating these computerized systems is difficult and laborious. Now, researchers at Carnegie Mellon University have shown they can rapidly build them by, in effect, teaching the computer to teach. Using a new method that employs artificial intelligence, a teacher can teach the computer by demonstrating several ways to solve problems in a topic, such as multicolumn addition, and correcting the computer if it responds incorrectly. Notably, the computer system learns to not only solve the problems in the ways it was taught, but also to generalize to solve all other problems in the topic, and do so in ways that might differ from those of the teacher, said Daniel Weitekamp III, a Ph.D. student in CMU's Human-Computer Interaction Institute (HCII). "A student might learn one way to do a problem and that would be sufficient," Weitekamp explained.

This Python for Machine Learning Tutorial will help you learn the Python programming language from scratch. You'll learn about Conditions and Functions in Python.

Python is one of the most preferred high-level programming languages, which is being increasingly utilised in data science and in designing complex machine learning algorithms. In one of our articles, we discussed why one should learn the Python programming language for data science and machine learning. In this article, we list down the top 9 free resources to learn Python for Machine Learning. About: This is a free class provided by the developers at Google. It includes written materials, lecture videos, and lots of code exercises to practice Python coding. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and Http connections.

Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often "machine learning algorithm" is used interchangeably with "machine learning model." Are they the same thing or something different? As a developer, your intuition with "algorithms" like sort algorithms and search algorithms will help to clear up this confusion. In this post, you will discover the difference between machine learning "algorithms" and "models."

Peloton has added a new groups feature to allow people to exercise together despite being in lockdown. The company – which makes internet-enabled spinning bikes and treadmills, as well as running an app of online exercise classes – has seen a huge surge in users in recent months as people have looked for a way to work out at home. Now it has added a new groups feature, officially called "tags", allowing people to track their exercise alongside other people in communities they have formed off the bike. With the new feature, users are able to add hashtags to their profile, which designate certain groups: a certain set of people all from the same workplace, for instance, or one of the many "tribes" of users that have formed on other platforms such as Facebook and Reddit. If a user has a given hashtag in their profile, they will be able to see what classes other members have taken and when they are working out.

Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on theory of evolution to deal with various types of mathematical problem, especially those related to optimization. Also Genetic Algorithms can be categorized as a subset of Metaheuristics, which are general-purpose tools and algorithms to solve optimization and unsupervised learning problems. In this series of video tutorials, we are going to learn about Genetic Algorithms, from theory to implementation. After having a brief review of theories behind EA and GA, two main versions of genetic algorithms, namely Binary Genetic Algorithm and Real-coded Genetic Algorithm, are implemented from scratch and line-by-line, using both Python and MATLAB. This course is instructed by Dr. Mostapha Kalami Heris, who has years of practical work and active teaching in the field of computational intelligence.

If you're new to all this deep learning stuff, then don't worry--we'll take you through it all step by step. We do however assume that you've been coding for at least a year, and also that (if you haven't used Python before) you'll be putting in the extra time to learn whatever Python you need as you go. You might be surprised by what you don't need to become a top deep learning practitioner. You need one year of coding experience, a GPU and appropriate software (see below), and that's it. You don't need much data, you don't need university-level math, and you don't need a giant data center.

Productive use of machine learning and artificial intelligence technologies is impossible without a platform that allows autonomous functioning of AI/ML mechanisms. In-platform AI/ML has a number of advantages that can be obtained via best practices by InterSystems.