We are drowning in content. Information in more readily available than ever before, but it has become overwhelming. How can employees find the content they need to be more productive and advance? In this webinar, we will discuss how artificial intelligence (AI) technology has been developed to solve the problem of content discovery by sifting through vast quantities of materials to make available the right information at the right time for the individual's needs.

Learn to build your own generative adversarial network using TensorFlow, with this free interactive tutorial, "General adversarial networks for beginners." If you ask a child to draw a cat, you'll learn more about the child than you will about cats. In the same way, asking neural networks to generate images helps us see how they reason about the information they're given. It's often difficult to interpret neural networks--that is, to relate their functioning to human intuition--and generative algorithms offer a way to make neural nets explain themselves. Neural networks are most commonly implemented as classifiers--models that are able to distinguish, say, an image of a cat from an image of a dog, or a stop sign from a fire hydrant.

What is Artificial Intelligence (AI)? Artificial Intelligence (AI) is the study of computer science focusing on developing software or machines that exhibit human intelligence. "AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future." Predictive systems– These AI are made to look at statistical data and form valuable conclusions. . Editing Software's– Here AIs suggest the ways that can be used to make pictures and texts more attractive. .

While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. It looks like f(n), as n tends to infinity, is infinitely smaller than log n, log(log n), log(log(log n))), and so on, no matter how many (finite number of) nested log's you have.

While composing this enriching this list of data science discussions, I found this awesome'poem' drafted statistically.

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To the inexperienced, hearing the words Artificial Intelligence (or AI) can make our ears turn off. It's hard to resist the urge – AI is a whole new language to most of us, one that has a stigma for being a real challenge to learn. CLWB.org has decided to help change this. Their upcoming event, Artificial Intelligence Demystified, aims to teach you the basics at the Engine Shed on 26 October. We caught up with Mike Lloyd, the Founder of CLWB, to find out more: "The objective of the event is to enable non-expert people to get on the front foot in terms of AI." Being able to manipulate the benefits AI presents is an invaluable skill and CLWB thinks its course could be the breakthrough point for you.

Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford course on the topic of Natural Language Processing with Deep Learning methods. This course is free and I encourage you to make use of this excellent resource. The course is taught by Chris Manning and Richard Socher.

Machine learning is easily one of the biggest buzzwords in tech right now. Over the past three years Google searches for "machine learning" have increased by over 350%. But understanding machine learning can be difficult -- you either use pre-built packages that act like'black boxes' where you pass in data and magic comes out the other end, or you have to deal with high level maths and linear algebra. This tutorial is designed to introduce you to the fundamental concepts of machine learning -- you'll build your very first model from scratch to make predictions, while understanding exactly how your model works. This tutorial is based on our Dataquest Machine Learning Fundamentals course, which is part of our Data Science Learning Path. The course goes into a lot more detail, and allows you to follow along writing code to learn by doing.

Are you interested in data science and machine learning? If yes, consider looking at the courses we list in this post. These online classes will help you get a head start in this field. You can start and stop at any time, no hard and fast rules at all. The best part is, these courses are often available for just $10 to $15. Content: The course consists of 277 lectures, a total of 40.5 hours of video lessons.