In a recent project, we found which of 700000 property parcels in Adelaide, Australia, contain swimming pools. We used a combination of crowdsourcing and supervised machine learning in order to harness the inherent ability of humans to identify objects in imagery and the speed of machines, which can perform this task much faster than humans, once trained sufficiently. Our initial approach consisted of training a random forest classifier with a set of crowdsourced labels, then using the machine classifications to present to the crowd only the parcels that were likely to contain swimming pools. Since only a small percentage of the parcels actually contain pools, the efficiency gain of this approach is huge compared to a pure crowdsourcing campaign. At first glance, identifying a pool in a high-resolution satellite image might appear to be a simple task for a human and a machine alike.

The notion of applying deep learning techniques to medical imaging data sets is a fascinating and fast-moving area. In fact, in a recent issue of IEEE's Transactions on Medical Imaging journal, there's a fantastic guest editorial on deep learning in medical imaging, that provides an overview of current approaches, where the field is headed, and what sort of opportunities exist. As such, we pulled out some of our favorite nuggets from this article and summarize/extend upon them in Q&A form, so they're more easily digestible. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. One way is via transfer learning, which has been used to overcome the lack of large labeled data sets in medical imaging.

Since the birth of the modern era of computing there has been an arms race between CPU microprocessor manufacturers that has pushed computer capabilities even higher, characterised by Moore's Law. This era's computer technology can be characterised as running sophisticated but essentially dumb applications. A new era is beginning that will drive microprocessor manufacturers to support intelligent applications, such as those based on newly emerged deep learning and other machine learning algorithms. Deep learning is the umbrella term for a set of techniques for architecting and training neural networks that in recent years has made huge leaps forward in accuracy. For example, deep learning neural networks are at the root of the most successful technologies for natural language understanding, image recognition, advanced game playing (such as Go), and others.

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

We hope you find it useful in your machine learning projects.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Join Jennifer Marsman as she welcomes Sonja Knoll to the show as they take a deep dive into Deep Learning as well as apply some real-world scenarios for you to try out on your own. If you're interested in learning more about the products or solutions discussed in this episode, click on any of the below links for free, in-depth information:

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted endeavor of building a “Creative Economy” and “Software Centric Society”. However, AI was not always flourishing as it is now. Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as ɛ-greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.

If you have feedback, please let us know in the ads subreddit. Who would like to start a collaborative Youtube channel that provides an explanation of various research papers? Aside from the Deep Learning Hype, What are some other interesting research topics for grad students coming into the field of statistics/machine learning? If a binary classifier (neural network model) achieves 99% training accuracy with 65% validation accuracy, what to do next?

Invented by Geoff Hinton, a Restricted Boltzmann machine is an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. Given their relative simplicity and historical importance, restricted Boltzmann machines are the first neural network we'll tackle. In the paragraphs below, we describe in diagrams and plain language how they work. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input, layer, and the second is the hidden layer. Each circle in the graph above represents a neuron-like unit called a node, and nodes are simply where calculations take place.