The release continues, "AI is creating new ways for people to engage with technology and with one another. Apple's Siri leverages natural language processing to recognize voice commands. Facebook's deep learning facial recognition algorithm can instantly identify a person with nearly 98 percent accuracy. And Amazon, Netflix and Spotify all utilize machine learning to understand how each item in their massive catalogs relates to the other and each customer's preferences. However, the technical expertise and infrastructure required to develop AI solutions are beyond the reach of most companies. They must bring together massive and diverse data sets, which requires significant engineering resources to manage complex data integration processes. Specialized predictive models must then be built to extract value from the data and continuously learn from it, requiring extensive data science expertise."

I am starting doing research on LSTM's. So far, all sources I have found just detail the use of some libraries and just mention that they use BPTT. I want to go into details of learning in LSTM's, an exact implementation with reasoning why they do things would be great.

Chatbots, are a hot topic and many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible. But with all the hype around AI it's sometimes difficult to tell fact from fiction. In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what's possible, and what will stay nearly impossible for at least a little while. Retrieval-based models (easier) use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers.

Streaming starts at 10am on Tuesday September 20th 2016. For more details, please visit www.deepmind.com/health.

This 2-hour tutorial will give you an introduction to deep learning and TensorFlow. We will introduce the fundamental concepts of deep learning, including Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Then we will introduce TensorFlow and its basics. You will get hands-on experience with TensorFlow, building a convolutional neural network in the class. You will have access to an AWS machine where TensorFlow is pre-installed by the instructor.

Researchers from Adobe and University of California, Berkeley developed software that automatically generates images inspired by the color and shape of the digital brushstroke. The software uses deep neural networks to learn the features of landscapes and architecture, like the appearance of grass or blue skies. Drawing a dark-colored, upside-down V triggers the AI to conjure a mountain or church steeple, where its seen the shape and color before. A blue line above that becomes a sky with various hues of blue, and a green line below becomes textured grass. Using CUDA, TITAN X GPU and the cuDNN version of the Theano deep learning framework, the researchers trained their models on more than 275,000 images of churches and landscapes.

Learn everything you need to design, train, and integrate neural network-powered machine learning into your applications with widely used open-source frameworks and the NVIDIA deep learning platform. As a perk, receive a certificate of attendance and free online training credits.

Are there any active communities (forums, Q&A) for reinforcement learning? For machinelearning in general there is this subreddit and the freenode irc channel for example. But their communities seem both to be mainly focused on deep learning. There is no freenode RL channel and the subreddit is quite dead. I am self-learning RL and often have questions (practical and theoretical ones) and am not quite sure where to ask or even where to get relevant news and an overview of the state of that field for.

SAS says its new visual data mining and machine learning software, available later this month, will "feed this need for smarter analytics". The company was speaking about its new solution at its Analytics Experience conference in Las Vegas attended by more than 10,000, on-site and online, to discuss business issues. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS analytics product manager. "This software helps provide positive outcomes to increase profitability, better understand customer behaviour and decrease the cost of doing business." Wexler said advanced analytics offer insight to businesses, but "machine learning and deep learning algorithms take it deeper, insights that were previously out of reach".

Artificial intelligence (AI) is no longer a futuristic concept, it's a staple of today. From virtual personal assistants like Siri and Cortana, to image scanners built to identify diseases, to Google's or Tesla's self-driving cars, AI is becoming a part of everyday technology. According to a MarketsandMarkets report, the artificial intelligence industry is estimated to reach USD 5.05bn by 2020, growing at an annual growth rate (CAGR) of 53.65% between 2015 and 2020. One reason for this significant growth is the increased use of machine learning technology – a subcategory of AI where computers learn from data themselves in the advertising and media industry. Machine Learning has a huge impact on the advertising ecosystem already.