AI News, BOOK REVIEW: Data science folks have 12 reasons to be thankful for this Thanksgiving

Data science folks have 12 reasons to be thankful for this Thanksgiving

Similarly, for the data science community, this year was filled with a number of new technologies, tools, version updates etc.

We invite data scientists, machine learning experts, and other data science professionals to come together on this Thanksgiving Day, and thank the organizations, which made our interactions with AI easier, faster, better and generally more fun.

This was a great news for Data science developers as Keras 2, a high- level neural network API allowed faster prototyping.

Data scientists were greeted by a fresh aroma of coffee, this April, as Facebook released the second version of it’s popular deep learning framework, Caffe.

Caffe 2 came up as a easy to use deep learning framework to build DL applications and leverage community contributions of new models and algorithms.

Caffe 2 was fresh with a first-class support for large-scale distributed training, new hardware support, mobile deployment, and the flexibility for future high-level computational approaches.

Apache MXNet, which is now available on AWS allowed developers to build Machine learning applications which can train quickly and run anywhere, which means it is a scalable approach for developers.

The new Cognitive Toolkit was now enterprise-ready, had production-grade AI and allowed users to create, train, and evaluate their own neural networks scalable to multiple GPUs.

September surprises came with the release of Splunk 7.0, which helps in getting Machine learning to the masses with an added Machine Learning Toolkit, which is scalable, extensible, and accessible.

Other features include seamless event annotations, improved visualization, faster data model acceleration, a cloud-based self-service application.

It was self scaling i.e., it instantly resized compute and storage without downtime with low manual administration costs.

It included multiple enhancements in Database Engine such as adaptive query processing, Automatic database tuning, graph database capabilities, New Availability Groups, Database Tuning Advisor (DTA) etc.

With features such as easy upgrades, Index sorting, better Shard recovery, support for Sparse doc values.There are other new features spread out across the Elastic stack, comprised of Kibana, Beats and Logstash.

It would also include a rewritten version of Hadoop shell scripts with bug fixes, improved compatibility and many changes in some existing installation procedures.

If not, be sure to give them a try, because 2018 is all set to overload you with new, and even more amazing tools, frameworks, libraries, and releases.

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