Voice technology and natural language recognition is at the forefront of AI development that is transforming our everyday life, and will undoubtedly be part of our future. Deep learning is at the core of identifying and processing voice for automation and new innovations. Join this Data Science Central webinar as conversational AI pioneer, Voicebox, shares their journey from an on-premise system that was manually intensive and costly to maintain, to an agile cloud platform that has allowed them to build, schedule, and run multiple production data pipelines that feed into their deep learning models with ease.

Used for object detection and more.

Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images. In the last few years, the field of machine learning has made tremendous progress on addressing these difficult problems. In particular, we've found that a kind of model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks -- matching or exceeding human performance in some domains.

This class is a great overview of the types of machine-learning models, and some of the history of how those models came into use. The fundamental explanations of complex ideas are generally excellent and very clear, but the practical equations that are necessary for implementations are difficult to understand for someone like me who is not used to reading abstract mathematical equations. Examples of equations that are worked out with explicit values are few and far between and this doesn't help. This makes the programming assignments exceptionally difficult even though the code they require is simple. Also, the amount of time required for this course is enormous, easily 10x what is predicted when there is a programming assignment.

The past two decades have seen the workplace transformed by digital advances. Gone are many traditional structures and practices, replaced with new ways of doing business, designed to support collaboration and digitally-enabled remote and flexible working. As the technology behind AI and robotics becomes more sophisticated, the number of jobs that remain untouched by automation will decrease. "To keep pace, businesses must rethink how they organise work, reinvent jobs, redeploy staff and implement robust plans for the future," says Lynda Gratton, professor of management practice at London Business School (LBS). There are also emerging social trends and shifting demographics to consider.

For this tutorial in my Reinforcement Learning series, we are going to be exploring a family of RL algorithms called Q-Learning algorithms. These are a little different than the policy-based algorithms that will be looked at in the the following tutorials (Parts 1–3). Instead of starting with a complex and unwieldy deep neural network, we will begin by implementing a simple lookup-table version of the algorithm, and then show how to implement a neural-network equivalent using Tensorflow. Given that we are going back to basics, it may be best to think of this as Part-0 of the series. It will hopefully give an intuition into what is really happening in Q-Learning that we can then build on going forward when we eventually combine the policy gradient and Q-learning approaches to build state-of-the-art RL agents (If you are more interested in Policy Networks, or already have a grasp on Q-Learning, feel free to start the tutorial series here instead).

There are ten webinars in four days, and SIG is hosting their Spring Summit in Washington D.C. from the 26th through the 29th (be sure to seek out Phil Ideson if you're going). If you like to plan further ahead, I recommend "Trade Wars & Supply Chains: Measuring and Managing the Impact" from Supply & Demand Chain Executive and Resilinc on April 26th at 1pm ET. Click on the title of each recommended webinar below to view the full description and register. BTW: If you haven't already, sign up for our mailing list to be sure you get my weekly recommendations in your Inbox each Monday. There has been so much hype around technologies such as AI, machine learning, and cognitive computing that they've practically achieved "IT SLICES!

Machine learning helps pinpoint errors in large datasets for cleansing before entering the analytics pipeline. This on-demand webinar shows you how to set it up. Big data brings tremendous opportunity to better target customers and improve operations. Yet, data-driven insights are only as good and trusted as the data going into them. Find out how you can build data quality into your structured, semi-structured, or unstructured data on Microsoft Azure Data Lake Store and HDInsight using Talend's native support for Spark machine learning algorithms.

Employees at all stages of their careers are challenged by the technological and socio-economical changes that are limiting the suitability of these employee's current skills and learning. Widening gap between the skills available and skills in demand is certainly alarming and you should not overlook a timely career advice. To brace yourself for a future-ready career you will require advanced technical training or specialized education. Dynamic re-skilling and learning on-the-go are keys to be successful in the competitive job market. Everybody is talking about Artificial Intelligence.

I've found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general.