A few quite exciting applications of deep learning in cancer research have appeared recently. This new algorithm can recognize cancer cells by looking at blood samples without introducing any intrusive chemicals in the process. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... The thumbnail image background was created by zhouxuan12345678 (CC BY-SA 2.0). Some blood cells were removed.

Note: Several people have asked me about data science and machine learning. Everyone is involved in Data Science. It is simply using knowledge and programs to turn lines of data into something useful, like a graph. If you've ever made a pie chart or a Pivot Table, you could consider yourself a data scientist. Data Science is a very hot field encompassing everything from problem scoping to UI design and visualization.

When you look at cloud skills, it's more important to think about what's coming rather than what's already here. Why? Thousands of IT people will complete cloud certification programs this year. If you delay, the job market may be flooded by the time you're ready. Soon the "Internet of Things" will be keeping watch on jet engines, refrigerators and freezers, factory floors and more, thanks to a series of partnerships announced by Microsoft at the Hannover Messe industrial fair in Germany. Some people spend weeks, months, even years trying to learn machine learning without any success.

This is an experimental and work-in-progress R library for easily using Google TensorFlow, inspired by Google skflow package. This package aims to help R users to easily build popular deep learning models as well as customized models with flexible and desired deep architectures. Note that the style of the way the package has been developed is new and experimental. Any feedback would be appreciated. Check out demos for available usages.

Rather recently, Microsoft released an app using AI to detect a dog's breed. In my non-SitePoint time, I also work for Diffbot – the startup you may have heard of over the past few weeks – who also dabble in AI. To test how they compare, in this tutorial we'll recreate Microsoft's application using Diffbot's technology to see if it does a better job at recognizing the adorable beasts we throw at it! We'll build a very primitive single-file "app" for uploading images and outputting the information about the breed under the form. If you'd like to follow along, please register for a free 14-day token at Diffbot.com, if you don't have an account there yet.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

When Pham Quang Hung started studying Japanese at Foreign Trade University in Hanoi in 1994, he never imagined that Vietnamese children would one day be able to learn the language in elementary school. Now the first secretary for educational affairs at the Vietnamese Embassy in Tokyo can hardly wait to see the launch in September of a pilot project to offer Japanese lessons at three elementary schools in Hanoi. It will be the first time that Japanese language education has been offered at the publicly run primary school level in Southeast Asia, according to Japanese officials. The project follows the development of a Japanese program that the Vietnamese government introduced for middle and high school students in 2003. At present, English and French are the only foreign languages Vietnamese students can learn in elementary school.

Specifically, the assignment of meaningful tags (annotations) to each unique data granule is best achieved through collaborative participation of data providers, curators and end users to augment and validate the results derived from machine learning (data mining) classification algorithms. The annotations provide curation, provenance and semantic (scientifically meaningful) metadata about the data source and the data object being studied. The design and specification of a unique, meaningful, searchable and scientifically impactful set of tags can be achieved through collaborative (human-plus-machine) annotation efforts and through discovery informatics research. These steps will produce a searchable classification and indexing scheme for the curation, classification, discovery, reuse, interoperability, integration and understanding of digital repositories.

Meaningful classification labels and metadata can be derived autonomously through machine intelligence or manually through human computation. Human computation is the application of human intelligence to solving problems that are either too complex or impossible for computers. For enormous data collections, a combination of machine and human computation approaches is required. Specifically, the assignment of meaningful tags (annotations) to each unique data granule is best achieved through collaborative participation of data providers, curators and end users to augment and validate the results derived from machine learning (data mining) classification algorithms. We see very successful implementations of this joint machine-human collaborative approach in citizen science projects such as Galaxy Zoo and the Zooniverse (http://zooniverse.org/).

In the previous blog we have seen the theory and mathematics behind the Maximum Entropy and Logistic Regression Classifiers. Logistic Regression is one of the most powerful classification methods within machine learning and can be used for a wide variety of tasks. Think of pre-policing or predictive analytics in health; it can be used to aid tuberculosis patients, aid breast cancer diagnosis, etc. Think of modeling urban growth, analysing mortgage pre-payments and defaults, forecasting the direction and strength of stock market movement, and even sports. Reading all of this, the theory[1] of Maximum Entropy Classification might look difficult. In my experience, the average Developer does not believe they can design a proper Maximum Entropy / Logistic Regression Classifier from scratch.