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Automated Text Generation & Data-Augmentation for Medicine, Finance, Law, and E-Commerce

This webinar teaches you how to leverage the human-level text generation capabilities of Large Transformer models to increase the accuracy of most NLP classifiers for Medicine, Finance, and Legal datasets with the Spark NLP library.

We will also explore the next-generation capabilities for E-Commerce and creative writing to enable the creation of automated marketing text.

Additionally, you will learn and understand intuitively how the mysterious generation parameters Temperature, Top-K-Sampling and Top-P-Nucleus sampling influence drawing from WordDistributions influences the generated text of Transformer Models. All automates and scales effortlessly to Industry Scale GPU or CPU clusters with the underlying Spark Engine.

About the speaker

Christian Kasim Loan
Lead Data Scientist

Christian Kasim Loan is a Lead Data Scientist and Scala expert at John Snow Labs and a Computer Scientist with over a decade of experience in software and worked on various projects in Big Data, Data Science and Blockchain using modern technologies such as Kubernetes, Docker, Spark, Kafka, Hadoop, Ethereum, and almost 20 programming languages to create modern cloud-agnostic AI solutions, decentralized applications, and analytical dashboards.

He has deep knowledge of Time-Series Graphs from his previous research in scalable and accurate traffic flow prediction and working on various Spatio-Temporal problems embedded in graphs at a Daimler lab.

Before his graph research, he worked on scalable meta machine learning, visual emotion extraction, and chatbots for various use cases at the Distributed Artificial Intelligence lab (DAI) in Berlin.

His most recent work includes the NLU library, which democratizes 5000+ state-of-the-art NLP models in 200+ languages in just 1 line of code for dozens of domains, with built-in visualizations and all scalable natively in Spark Clusters by its underlying Spark NLP distribution engine.