Eremenko really you are amazing guy. I also like your tutorials a lot. For sure you are doing great things to change world. God Bless U

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Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution.  It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE.  And boy oh boy can it be tough to explain.  But we're trying our hardest in this episode!

KL Divergence

In this episode of the O’Reilly Media Podcast, I spoke with Gayle Sheppard, vice president and general manager of Saffron AI Group at Intel, and David Thomas, chief analytics officer for Bank of New Zealand (BNZ). Our conversations centered around the utility of artificial intelligence in the financial services industry.

Associative Memory AI: Enabling Machines to Fight Financial Crime With Human-Like Reasoning

In this episode of the SuperDataScience Podcast, I chat with the the founders of Cigen, Leigh and Daniel Pullen. You will listen to an overview of RPA or the Robotics Process Automation, talk what it’s all about, how it works, how business gets value from it and also how people can learn this and integrate to their career. You will learn the different RPA tools or the global tools that any business can use across any applications. Also, you will know the concept of Robots-for-Hire and how it is valuable for entrepreneurs.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/173

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 173: Understanding Robotics Process Automation (RPA) to Disrupt Your Business

Convex optimization is one of the keys to data science, both because some problems straight-up call for optimization solutions and because popular algorithms like a gradient descent solution to ordinary least squares are supported by optimization techniques. But there are all kinds of subtleties, starting with convex and non-convex functions, why gradient descent is really an optimization problem, and what that means for your average data scientist or statistician.

Convex (and non-convex) Optimization

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The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

SDS 173: Understanding Robotics Process Automation (RPA) to Disrupt Your Business

Super Data Science: ML & AI Podcast with Jon Krohn

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