Deep learning and deep reinforcement learning have recently been successfully applied in a wide range of real-world problems. Here are 15 online courses and tutorials in deep learning and deep reinforcement learning, and applications in natural language processing (NLP), computer vision, and control systems. The courses cover the fundamentals of neural networks, convolutional neural networks, recurrent networks and variants, difficulties in training deep networks, unsupervised learning of representations, deep belief networks, deep Boltzmann machines, deep Q-learning, value function estimation and optimization, and Monte Carlo tree search. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is a great open access textbook used by many of the courses, and Daivd Silver provides a good series of 10 video lectures in reinfrocement learning. For machine learning reviews, here are 15 online courses and tutorials for machine learning.

PROVIDENCE, R.I. [Brown University] -- The past few years have witnessed a revolution in artificial intelligence. AI systems are beating humans on reading comprehension tests, clobbering board game champions and enabling cars to drive themselves. Even more mundane AI systems, like smartphone apps that recognize faces and personal assistants that understand verbal commands, were seemingly insurmountable challenges just a decades or so ago. These recent breakthroughs have been made possible in large part by a technology known as deep learning or deep neural networks -- algorithms that have become the unseen force behind modern AI. Every time a phone responds to "Hey Siri," or Google translates a sentence from Swedish to Swahili, deep neural networks are at play.

Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels. Our experiments on concept prerequisite relations show our proposed framework can substantially improve the classification performance with the same query budget compared to other baseline approaches.

This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or research&development. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about backtracking: we will talk about problems such as N-queens problem or hamiltonian cycles and coloring problem. In the second chapter we will talk about dynamic programming, theory first then the concrete examples one by one: fibonacci sequence problem and knapsack problem.

This post is from guest contributor Steve Lattanzio from MetaMetrics. While we do not tend to cover college rankings at e-Literate, we do care about transparency in usage of data as well as understanding opportunities where technology and data might inform students, faculty, administrators and the general educational community. The following post is an interesting exploration in the usage of the full set of College Scorecard data in a way that is understandable and usable. Ranking colleges has become a bit of a national pastime. There are many organizations that publish "overall" rankings for our institutions of higher education (such as Forbes, Niche, Times Higher Education, and US News & World Report), each with their own methodologies. We don't typically get the complete and precise picture of how these rankings are constructed.

Microsoft and Chinese retailer Alibaba independently announced that they had made software that matched or outperformed humans on a reading-comprehension test devised at Stanford. Microsoft called it a "major milestone." Media coverage amplified the claims, with Newsweek estimating "millions of jobs at risk." Those jobs seem safe for a while. Closer examination of the tech giants' claims suggests their software hasn't yet drawn level with humans, even within the narrow confines of the test used.

At the top of Mount Olympus, Hephaestus – the Greek god of fire and a blacksmith – built a palace for his fellow deities. Inside, he crafted robotic golden statues to serve them. Greek mythology tells us that a fascination with the robotic is as old as western civilization. In the intervening centuries, so advanced have AI, robotics and digital technologies become that sometimes it's easy to think we're being overtaken by innovation. Yet to hide in fear of a jobless apocalypse and a grim vision of rule by robots is to focus on only one side of a complex argument. Flip this negativity on its head and we see that, far from dominating life, technology could be used to help define and complement the human in all of us.

Non-Salesforce app developers may be missing out on a hidden gem in the AI world. When developers think of using the cloud for AI, they might think of IBM Watson, Microsoft Azure Cognitive Services, Google Cloud, or Amazon AI. When they hear of Salesforce Einstein, they might automatically assume it's limited to the Salesforce developer specialization. Any app, whether Salesforce-related or not, can leverage the sophisticated AI cloud technologies that Salesforce has acquired. They've entered the AI market with Salesforce Einstein, their own orchestration of AI cloud services.

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

Dubai based startup, Black Cube Solutions, a frontier tech company, is poised to disrupt the learning and education sector by focusing on skill development and career progression through machine learning and artificial intelligence. Black Cube Solutions has recently unveiled the world's first complete platform for skill development for university students, graduates and working professionals powered by artificial intelligence and machine learning. It's the only platform that brings together all the stakeholders of the skill development ecosystem including universities, corporates, training providers and executive education providers. Peer Mohaideen Sait, Founder and CEO, Training Calendar said, "Skill development is the most pressing challenge for governments and corporates across the world. That is why we came up with the idea of Training Calendar. Our product is aligned to the UAE's national strategy on innovation and is focused on skill development."