Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.

Few days back I went to a bar with couple of friends and found that one of my friends is working with the bartender to create a perfect cocktail. The scene was such that it got me thinking. The bartender's action could very well be used to explain how an analytics lead could get his machine learning deployed and what best practices are needed. A good bartender keeps his vocabulary updated with what all liquor and additives at his disposal, so that he could create a variety that specifically targets your experience. Similarly, having an open mindset will help in picking the tool that could best serve the problem and not the bias?

New lip-reading technology developed at the University of East Anglia (UEA) could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Dr Helen L. Bear and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Dr Bear said. Dr Bear, whose findings will be presented at the International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Shanghai on March 25, said unique problems with determining speech arise when sound isn't available - such as on CCTV footage - or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation. Dr Bear said: "We are still learning the science of visual speech and what it is people need to know to create a fool-proof recognition model for lip-reading, but this classification system improves upon previous lip-reading methods by using a novel training method for the classifiers. "Potentially, a robust lip-reading system could be applied in a number of situations, from criminal investigations to entertainment.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created an algorithm that can predict how memorable or forgettable an image is almost as accurately as humans -- and they plan to turn it into an app that subtly tweaks photos to make them more memorable. For each photo, the "MemNet" algorithm -- which you can try out online by uploading your own photos -- also creates a heat map that identifies exactly which parts of the image are most memorable. "Understanding memorability can help us make systems to capture the most important information, or, conversely, to store information that humans will most likely forget," says CSAIL graduate student Aditya Khosla, who was lead author on a related paper. "It's like having an instant focus group that tells you how likely it is that someone will remember a visual message." Team members picture a variety of potential applications, from improving the content of ads and social media posts, to developing more effective teaching resources, to creating your own personal "health-assistant" device to help you remember things.

New lip-reading technology developed at the University of East Anglia (UEA) could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Dr Helen L. Bear and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Dr Bear said. Dr Bear, whose findings will be presented at the International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Shanghai on March 25, said unique problems with determining speech arise when sound isn't available - such as on CCTV footage - or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation. Dr Bear said: "We are still learning the science of visual speech and what it is people need to know to create a fool-proof recognition model for lip-reading, but this classification system improves upon previous lip-reading methods by using a novel training method for the classifiers. "Potentially, a robust lip-reading system could be applied in a number of situations, from criminal investigations to entertainment.

We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.

I've just finished Week 5 of the Coursera/Stanford Machine Learning course. It has been a mixture of refreshing, relearning, and new for me. I had already been using, building, and researching/evaluating machine learning algorithms for a number of years. I therefore felt like I'knew' a lot of the concepts, particularly the introductory ones. I put'knew' in quotes, however, since I've always had a feeling that I don't know them well enough, no matter how many times I've used them.

Linear regression is one of the simplest machine learning techniques you can use. It is often useful as a baseline relative to more powerful techniques. Like all regressions, we wish to map some input X to some input Y. You may recall from your high school studies that this is just the equation for a straight line. When X is 1-D, or when "Y has one explanatory variable", we call this "simple linear regression".

After a first all-day introduction to Deep Learning back in February, the Deep Learning class will continue in April and May. These classes will mostly be hands-on workshops (please don't forget to bring your laptop), with a minimum of formal theory to support these. Programming level: Beginner to advanced. Important: We will check IDs at the entrance. You will not be able to enter the school if you are not on the list. Please give us your real name when you register to this meetup.

In an announcement made on Wednesday at its GCP Next conference in San Francisco, tech giant Google said that it is rolling out the private beta of a new Cloud Machine Learning service which will enable businesses to create a custom machine learning model for predicting the future of their ventures. According to the details shared by Google, the Cloud Machine Learning service has the capability to handle data ingestion and training, and subsequently make use of the resultant machine-learning model to make predictions for a business' future. Google said that for building a custom machine learning model that can make future predictions for the future of a business, users of the Cloud Machine Learning service need to work with data which they have stored in Google's other cloud services. In a demonstration of the creation of a custom machine learning model for predicting the future of a business, Jeff Dean -- the chief of Google's Brain deep-learning research project -- showed how the Cloud Machine Learning service could build a model which predicts a click by a consumer on an advertisement. The model demonstrated by Dean, to show how the Cloud Machine Learning service works, was based on marketing software firm Criteo's anonymized data pertaining to consumers' chances of clicking on an advertisement.