IJCAI 2016 Conference

[This is to claim the ownership of this article due to non-payment from the client. Please do not reprint this article in any sites without permission.]

In this era of increasing focus being laid on artificial intelligence, the recent conference held in July 2016 had geniuses and teachers from varied international destinations come together to New York City to discuss their studies and speculations. These people who had gathered at the conference have done research, and some have even checked the commercial uses. The trending topics in the conference were Machine Learning (ML), deep learning (DL) and natural language processing (NLP). The conference also has an official website http://www.ijcai-16.org which is specifically for the 2016 conference. The website has some interesting guides and workshops related to the subjects of the discussion. The tutorials cover a vast area in artificial intelligence, how it can be used in future and also the challenges faced by commercial purposes.

Tutorial of Deep Learning

One of the topics in the tutorial was “ Deep Learning and Continuous Representations for NLP”. The tutorial has a brief outline of the background history of deep neural networks. The background information also consists of details of examples of neural network models which have been used for classification of queries. It also gives an outline about the various forms of DNN which has been used in differentiation, ranking and generative tasks. There are also several technological breakthroughs that have been made possible with DNN. The tutorial also provides some practical example cases of DNN application like

• Machine lead translation of language
• Searching the internet
• Adding text headings to images
• Providing answers to questions
• Connecting entities within the context
• Selecting advertisements

Development of CNTK

These concepts are rather complex and thus difficult to be understood by most people. It consists of complicated solutions and mathematical algorithms and thus only an individual who has immense knowledge in Mathematics, algorithms, neural networks and computations are able to understand these concepts. This has been simplified with the Microsoft Computational Network Toolkit (CNTK) which is a straightforward and efficient learning method for those people who are looking to work on learning and understanding the machine learning techniques and deep learning. Learning tools are used mostly for neural networks. These learning machines are employed in computational systems producing random logics as several computational steps govern it.

Advantage of CNTK Software

With the CNTK system, the user will just have to process the computational network and describe it well. Once this is done, the system carries on all the other computations from there on, and the parameters that govern the system automatically handles the network. Before CNTK was developed, most of the deep learning tasks took several weeks to finish and this process has been accelerated with the newest development in DNN. Several studies conducted by researchers in recent times have brought about major advancements in DNN field development, and the systems are now closely related to human brain system. It is the reason why neural networking development has been closely related to development in artificial intelligence.

CNTK has been released by Microsoft as an open source software, and can be downloaded for free usage from Codeplex or GitHub. CNTK was developed by a group of volunteers who set out to find a solution to make improvements in the computer understanding of speech as they were using tools that were slower to operate. CNTK was found to be more effective than the previously used four other popular software for deep learning. It had a better capacity to understand speech and image when compared to the other software in use and thus had depicted better communication function.

Downloading CNTK

One of the factors that make CNTK different from the others is that it can be used any person even on a single computer and also for huge purposes on several GPU systems. For any help regarding the download, installation and usage of the software to your local system, you can find tutorials for using CNTK right from download to how to do operations with the toolkit. BrainScript network language is used in the toolkit, and if you look at the tutorial, the first chapters are done using logistical variables.

It can be used on multiple platforms like Linux, Windows, Azure and even Docker container. The source code is easily available online from where it can be downloaded, and the execution package can be created with Windows OS.