Skip all the talk and go directly to the Github Repo with code and exercises. Reinforcement Learning is one of the fields I'm most excited about. Over the past few years amazing results like learning to play Atari Games from raw pixels and Mastering the Game of Go have gotten a lot of attention, but RL is also widely used in Robotics, Image Processing and Natural Language Processing. Combining Reinforcement Learning and Deep Learning techniques works extremely well. Both fields heavily influence each other.

When millions of people across the world tuned in to watch DeepMind's machine beat the human Go world champion Lee Sedol, they also witnessed a historic victory for open-source. DeepMind used a scientific computing framework called Torch extensively in the development and execution of AlphaGo's neural networks. Torch was first released back in 2002 under a BSD open-source license with algorithms that are still commonly used by data scientists such as multi-layer perceptrons, support vector machines and K-nearest neighbours. Torch also supported ensembles -- a popular technique that combines the output of multiple algorithms, usually with a weighted average. It's not just open-source software that contributed to the growth of machine learning.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight," said Hugo D'Ulisse, Head of Analytical Platform, SAS UK & Ireland.

Philip Koopman, professor of Carnegie Mellon Univ., believes the biggest hole in a Federal Automated Policy published late Sept. is in the regulators' failure to tangle head-on with fundamental difficulties in testing Machine Learning -- a problem already known to the scientific/engineering community. Representativeness of data Carmakers are building a fake city, for example, in Michigan to test autonomous vehicles. What's important, though, is whether the test data represents real-world driving conditions? A highly autonomous vehicle is designed to operate only in a certain designated area such as "driving only in downtown Pittsburgh." In DoT lingo, this concept is the "Operational Design Domain."

Currently, many of us are overwhelmed with mighty power of Deep Learning. We start to forget about humble graphical models. CRF is not so trendy as LSTM, but it is robust, reliable and worth noting. In this post, you will find a short summary about CRF (aka Conditional Random Fields) – what is this thing, what is it for and some interesting facts. In practical implementation, the computational time is often larger due to many other operations like numerical scaling, smoothing etc.

Some of the world's biggest tech companies are teaming up to address the future of artificial intelligence and how it will affect privacy, safety, interoperability and collaboration between people and AI. Amazon, Google's DeepMind division, Facebook, IBM and Microsoft have founded the Partnership on Artificial Intelligence (PAI) to consider how and why AI developments, such as online facial recognition, might be a cause for concern. "Every new technology brings transformation, and transformation sometimes also causes fear in people who don't understand the transformation. One of the purposes of this group is really to explain and communicate the capabilities of AI, specifically the dangers and the basic ethical questions," said Yann LeCun, Facebook's director of AI. LeCun advises that the group will foster communication among those who build AI, bring in additional opinions from academia and civil society and inform the public on the progress of AI.

I have basic understanding of ML. I have built a few regression models and ended top 10% in Kaggle. I have good experience in Python using NLTK. I have access to Tensorflow/GPU. My interests are in building Chatbots for customer support.

The Chinese search behemoth, Baidu, announced a 200m investment initiative focused on AI and ran a pre-release of their new open source deep learning framework called PaddlePaddle. The company has a ways to go to compete given that developers are still more likely to use TensorFlow, which holds the lead along with Caffe, Keras and Theano. Speaking of the growing number of hardware and software configurations available today, this research paper provides helpful benchmarks. Backchannel run a rare piece on how Apple uses machine learning. It states that a 200mb software package runs on the iPhone encompassing "app usage data, interactions with contacts, neural net processing, a speech modeler and a natural language event modeling system".

A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.

We describe an application of neural networks to predict the placements of resources in StarCraft II maps. Networks are trained on existing maps taken from databases of maps actively used in online competitions and tested on unseen maps with resources (minerals and vespene gas) removed. This method is potentially useful for AI-assisted game design tools, allowing the suggestion of resource and base placements consonant with implicit StarCraft II design principles for fully or partially sketched heightmaps. By varying the thresholds for the placement of resources, more or fewer resources can be created consistently with the pattern of a single map. We further propose that these networks can be used to help understand the design principles of StarCraft II maps, and by extension other, similar types of game content.