This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. You learned about backpropagation (and because of that, this course contains basically NO MATH), but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

Machine learning is increasingly becoming mainstream. It promises higher accuracy and better ROI, and has started to emerge as one of the more reliable analytical practices in recent times. It provides organizations with an edge over their competition. That said, building the right team is a tricky challenge. The traditional approach of building an in-house team for same is not only cumbersome, but also takes lot of time to scale.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

When students enrolled in USC's daunting neuroscience graduate program needed help cracking a tough project, they all went to Bosco Tjan. It didn't hurt that his advice often came with a free cappuccino. Mara Mather, a professor of gerontology and psychology at USC, described Tjan as an affable, caring presence on campus. He always found time to aid students and professors despite a breathless schedule. In many ways, she said, Tjan was the center's heartbeat.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

Another essential skill in data analysis is data . Visuals are extremely important for both exploratory data analysis, as well the communication of your results. Matplotlib is the most commonly used library for this in Python. Get inspired by viewing some plots and graphs: Matplotlib Gallery Take a look at some sample code: Matplotlib Examples Review the Matplotlib chapter on DataCamp: DataCamp Python for Data Science Come up with some visualizations for your toy dataset.

This video will get you up and running with your first AI Writer able to write a short story based on an image that you input. I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Paper on skip thought vectors: http://arxiv.org/pdf/1506.06726v1 You can test this code out at this site! It's really cool, they have a bunch of deep learning models in the cloud, you just have to upload an input and it gives you an output: http://www.somatic.io/models/2n6g7RZQ

It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or AI) is an area of computer science where computers are "developed" to behave much the way humans do.

Udacity 2,479 views What is machine learning?

Starred articles are candidates for the picture of the week. A comprehensive list of all past resources is found here. We are in the process of automatically categorizing them using indexation and automated tagging algorithms. IBM makes quantum computing available in the cloud 2016 Big Data 100: 20 Coolest Platform And Tools Vendors The fight against antimicrobial resistance across Europe Cool video pie chart Inside Facebook's Biggest Artificial Intelligence Project Ever How to tell two radically different stories from the same dataset Data science, no coding required: DataRobot's automated platform Google launches new machine learning platform TechCrunch Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Scienc... Forbes Beyond the hype: the hard work behind analytics success MIT Sloan Deep learning will be huge -- and here's who will dominate it Years You Have Left to Live, Probably - Nice interactive chart by FlowingData Alooma gets $11.2 million Series A to solve data science pain points AI program wrote a short novel, and almost won a literary prize How facial recognition can expose your life to strangers Data science, no coding required: DataRobot's automated platform Deep learning will be huge -- and here's who will dominate it Alooma gets $11.2 million Series A to solve data science pain points