Natural language understanding at scale with Spark NLP
February 11, 2020
David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (NLP) using the highly performant, highly scalable open source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve.
A hands-on introduction to natural language processing in Python
February 1, 2020
With the advent of voice-based assistants and chatbots in our homes, our phones, and our computers, businesses, stakeholders, and developers want to learn about language processing. Grishma Jena introduces you to natural language processing (NLP) using Python. You'll start off with textual data and learn how to process it to derive useful insights that can be used in real-world applications.
Practicing data science: A collection of case studies
January 7, 2020
Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
Industrialized capsule networks for text analytics
December 30, 2019
Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks.
Operationalizing real-time ML and DL with GigaSpaces, Intel Analytics Zoo, and Optane DC Persistent Memory
December 29, 2019
Yoav Einav and Vin Costello explain how to achieve faster analytical processing, leveraging in-memory performance for the cost of flash with persistent memory (~300% faster than SSD); smarter insights at optimized TCO, scaling the speed layer capacity for smarter real-time analytics with 7x lower footprint); and Agile automation of your ML and DL model CI/CD pipeline, for faster time to market.
Architecting Enterprise Platforms for CI/CD, Cloud and SRE with AI and Analytics
December 26, 2019
Are machines ready to write, test, debug and patch enterprise software? What are the emerging capabilities? Is it a fantasy or fast happening already? How are enterprises strategizing and retooling?Gi …