These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.

In this episode of Robots in Depth, Per Sjöborg speaks with Ian Bernstein, the founder of several robotics companies including Sphero. He shares his experience from completing 5 successful rounds of financing, raising 17 million dollars in the 5th one. Ian also talks about building a world-wide distribution network and the complexity of combining software and hardware development. We then discuss what is happening in robotics and where future successes may come from, including the importance of Kickstarter and Indiegogo. If you view this episode, you will also learn which day of the week people don't play with their Sphero:-).

GitHub is much more than a software versioning tool, which it was originally meant to be. Now people from different backgrounds and not just software engineers are using it to share their tools / libraries they developed on their own, or even share resources that might be helpful for the community. Following the best repos on GitHub can be an immense learning experience. You not only see what are the best open contributions, but also see how their code was written and implemented. Being an avid data science enthusiast, I have curated a list of repositories that have been particularly famous in the year 2017.

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. In this post, you will discover the problem of exploding gradients with deep artificial neural networks. A Gentle Introduction to Exploding Gradients in Recurrent Neural Networks Photo by Taro Taylor, some rights reserved. An error gradient is the direction and magnitude calculated during the training of a neural network that is used to update the network weights in the right direction and by the right amount.

Giving specific tasks to AI is quite easy for engineers and programmers, but designing a codebase that gives artificial intelligence the ability to adjust on the fly is not easy. "It turns out those things are really hard," said Cynthia Breazeal, a roboticist at the Massachusetts Institute of Technology's Media Lab …

Decision Tree learning is used to approximate discrete valued target functions, in which the learned function is approximated by Decision Tree. To imagine, think of decision tree as if or else rules where each if-else condition leads to certain answer at the end. You might have seen many online games which asks several question and lead to something that you would have thought at the end. A classic famous example where decision tree is used is known as Play Tennis. If the outlook is sunny and humidity is normal, then yes, you may play tennis.

A person's university years should be all about expanding your horizons, as well as meeting people with perspectives and backgrounds different from your own. Well, what could be more different than sharing your classroom with a robot? That's what 31 philosophy students at Notre Dame de Namur University in California recently experienced when they were joined in their "Philosophy of Love" program by Bina48, an A.I. animatronic robot. The robot participated via Skype in a series of sessions before appearing "in person" in the final class. "I wasn't sure how the students would react, but they were psyched about it," Professor William Barry, an associate professor of philosophy at Notre Dame de Namur, told Digital Trends.

Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.

This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)

A non-comprehensive list of awesome things other people did in 2014 Last year I made a list off the top of my head of awesome things other people did. I loved doing it so much that I'm doing it again for 2014... The Current State of Machine Intelligence I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find -- my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then... The Things I Wish I Knew - Lessons Learned from Making Data Product Talk from DJ Patil, Greylock Partners as part of this seminar series featuring dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field A Data Analyst's Blog Is Transforming How New Yorkers See Their City It may have been the fire hydrants that certified Ben Wellington as the king of New York's "open data" movement.