This year, the world witnessed significant technology advancements that will have long term implications for our economy and the way we work. Artificial intelligence dominated the list of top courses this year, taking three of the spots in our top 10 list, including the Machine Learning and Deep Learning courses taught by our co-founder Andrew Ng and a Machine Learning course from the University of Toronto. Blockchain has also burst onto the scene, putting Princeton's Bitcoin and Cryptocurrency course at number five on the list.

As 2017 winds down, trend watchers are looking ahead to 2018 and thinking about the trends taking shape. Artificial Intelligence is top of mind for many. What is Artificial Intelligence (AI), and what is the difference between AI, Machine Learning and Deep Learning? According to techopedia, "Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans." AI computers might be used for speech recognition, learning, planning and problem solving.

About this course: The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

This hebbian/anti-hebbian rule (see below) efficiently converges deep models in the context of a Reinforcement Learning regime. In a nutshell the rule says if there is no pre-synaptic spike then there will be no weight change (to preserve connections that were not responsible). Otherwise the direction of weight change will depend on whether a post-synaptic spike occured and whether there was a reward. I have not been able to find much existing work re: local rules for deep models, however it's quite likely this rule exists elsewhere..

Even with so many deep learning papers coming out this year, there were a few publications I felt managed to rise above the rest. Here are the five papers that impacted my mental models the most over the last year. For each, I state the "goal" of the paper, briefly summarize the work, and explain why I found it so interesting. Rather than describe exactly what the authors did here, I'll let some of the incredible results stand on their own: These stunning images are from the CycleGAN paper, in which the authors learn a pair of translation networks capable of translating between unpaired sets of images. These stunning images are from the CycleGAN paper, in which the authors learn a pair of translation networks capable of translating between unpaired sets of images.

Microsoft's Cognitive Toolkit (better known as CNTK) is a commercial-grade and open-source framework for deep learning tasks. At present CNTK does not have a native R interface but can be accessed through Keras, a high-level API which wraps various deep learning backends including CNTK, TensorFlow, and Theano, for the convenience of modularizing deep neural network construction. The latest version of CNTK (2.1) supports Keras. The RStudio team has developed an R interface for Keras making it possible to run different deep learning backends, including CNTK, from within an R session. This tutorial illustrates how to simply and quickly spin up a Ubuntu-based Azure Data Science Virtual Machine (DSVM) and to configure a Keras and CNTK environment.

Artificial intelligence (AI) is booming in all areas of business and medicine. But it's taken a special hold in radiology. RSNA 2017 had its first machine learning showcase, highlighting the companies and technologies already changing how radiology is practiced. About 30 machine learning companies demonstrated their capabilities. AI brings a mix of feelings to those who practice radiology, including fear, hope, and hype, said Woojin Kim, MD, a Chief Information Officer for Nuance's health care division, and an MSK radiologist.

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you're looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries.

AI technologies are some of the most complex and exciting topics going into the new year and so we've put together our list of the top 10 artificial intelligence trends to look out for in 2018. Machine learning (ML) is the branch of artificial intelligence that focuses on equipping machines with the ability to learn without human intervention or programming. Various kinds of machine learning platforms are currently available for businesses and enterprises to begin to take advantage of AI. Different approaches to machine learning will most likely be common throughout 2018 as hybrid and deep learning models are combined so as to account for and model uncertainty. Cloud and edge-based machine learning platforms will also most likely become more prolific and widely adopted throughout 2018 as these technologies become cheaper for both service providers and end users.

The "deep learning" boom continued in 2017, with another year of rapid advances in artificial intelligence and machine learning. Beyond the technical wonderment, the conversation also deepened around the role of such technologies in our society. Below are a few of our most significant stories and commentary on the subject this year. Google's AlphaGo game-playing software grew in ability by leaps and bounds, leaving not only the greatest human masters in the dust but also its own precursors. Experts called for governments to face the imminent threat of autonomous killer robots.