The CSNET project, sponsored by the National Science Foundation, has as its goal the design and implementation of a computer communications network to provide services to computer science research groups in the United States. Experience with Arpanet has shown that access to a computer network can lead to significantly higher level of interaction between geographically dispersed researchers. In this regard, a goal of CSNET is to foster "universal connectivity', i.e., participation by every university, industrial, and government computer science research group in the United States. By December 1982, over eighty university, government, and industrial sites will be participating.
Machine learning is the future of automation. Millions of tasks performed by humans on a daily basis will be eventually replaced by neural networks trained. Even now, machine learning algorithms shape your life. The job market is shifting to accommodate this new technology, and those who are capable of programming their own networks (or integrating with existing ones) are in high demand. There has never been a better time to dive into machine learning.
Convolutional Networks have been hugely successful in the field of deep learning and they are the primary reason why deep learning is so popular right now! They have been very successful, but they have drawbacks in their basic architecture, causing them to not work very well for some tasks. CNN's detect features in images and learn how to recognize objects with this information. Layers near the start detecting really simple features like edges and layers that are deeper can detect more complex features like eyes, noses, or an entire face. It then uses all of these features which it has learned, to make a final prediction.
Josh Patterson currently runs a consultancy in the big data machine learning / deep learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he broughtHadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 17 years in software development and is very active in the open source space contributing to projects such as deeplearning4j, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.