Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on. Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed. Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning.

Why'Deep Learning' is called deep? It is because of the structure of ANNs. Earlier 40 years back, neural networks were only 2 layers deep as it was not computationally feasible to build larger networks. Now it is common to have neural networks with 10 layers and even 100 layer ANNs are being tried upon. Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans. Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics . Deep learning does this job automatically and make life easier.

Microsoft is releasing its Computational Network Toolkit (CNTK) on GitHub, making the very efficient AI tools used by its own researchers available to the broad developer and data science community. Xuedong Huang, Microsoft's Chief Speech Scientist, and his team were anxious to make faster improvements to how well computers can understand speech, and the tools they had to work with were slowing them down. So, a group of volunteers set out to solve this problem using a homegrown solution that stressed performance over all else. CNTK is the outcome of that project, and has proved more efficient than other popular computational toolkits used by developers to create deep learning models for speech and image recognition. Microsoft is internally using CNTK on a set of powerful computers that use graphics processing units, or GPUs.

Microsoft is making the tools that its own researchers use to speed up advances in artificial intelligence available to a broader group of developers by releasing its Computational Network Toolkit on GitHub. The researchers developed the open-source toolkit, dubbed CNTK, out of necessity. Xuedong Huang, Microsoft's chief speech scientist, said he and his team were anxious to make faster improvements to how well computers can understand speech, and the tools they had to work with were slowing them down. So, a group of volunteers set out to solve this problem on their own, using a homegrown solution that stressed performance over all else. "The CNTK toolkit is just insanely more efficient than anything we have ever seen," Huang said.