Every Python project can benefit from automation using Makefile, optimized Docker images, well configured CI/CD, Code Quality Tools and more…
Looking at data one way can tell one story, but sometimes looking at it another way will tell the opposite story. Understanding this paradox and why it happens is essential, and new tools are available to help automatically detect this tricky issue in your datasets.
In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.
Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.
Everybody knows how important is understanding your customer, but how to do that in an era of Information Overload?
Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster.
A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.
Will You Enroll At #Google University For $49/Month? On @Kaggle some prizes are only for Americans - here are international alternatives; Advanced #NumPy for #DataScience; Free From MIT: Intro to Computer Science and Programming in Python
DeepMind has done some of the relevant work in the area of simulating imagination in deep learning systems.
We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.
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