Abstract: 

Objective: Natural Language Processing (NLP) is the fastest-growing field of deep learning with interest and funding from top AI companies to solve problems of language, text, and unstructured information. This has resulted in a tremendous focus on model building that combines language, mathematics, and computer science.
This workshop will focus on problems of text summarization, question answering, and sentiment classification using modern approaches to model-building (GNMT, BERT, and GPT2). We will apply this to real-world problems to create an NLP pipeline on top of the PyTorch framework and spaCy.
Learning Outcomes: At the end of this workshop, you will have a working knowledge of the PyTorch API to train your own deep learning models. You will be able to use OpenVINO to run model optimizer to use less compute and memory for deploying model inference in production.

Session Outline
1. Natural Language Process & Transfer Learning
2. Fundamentals and application of Language Modeling Tools
3. Use NLP pipeline to process documents, Word Vectors
4. Introduction to SpaCy and PyTorch
5. Introduction to pre-trained models such as BERT
6. Sentiment analysis
7. Text summarization

Background Knowledge
Python coding skills, intro to PyTorch framework is helpful, familiarity with NLP

Bio: 

Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.