Abstract: Deep Learning is an incredibly powerful technique, which has found uses in wide range of applications such as image object detection, speech translation, natural language processing and time series modeling. However, training deep neural network models requires a tremendous amount of time, training data and compute resources. A technique called transfer learning allows data scientists to increase their productivity dramatically by sharing neural network architectures and model weights. Reuse of a pre-trained model on a different but related task enables training of deep neural networks with comparatively less data. In this talk, you will learn the details of how transfer learning works and will see demonstrations in both financial and healthcare domains. We will talk about specific use cases and lessons learned that are applicable to many other industry sectors.
Bio: Anjali is a Senior Data Scientist at IBM aligned to insurance and financial services industry. She has worked across healthcare, financial services and telecommunications industries. Her expertise in applying cutting-edge technology to analyze structured and unstructured data has helped her clients convert data into actionable business insights. Her early career in software engineering focused on managing complex projects with strict deadlines (having delivered multiple technology solutions). Prior to joining IBM, she has delivered 80+ lectures as Assistant Professor in Health Information Management. She has a Ph.D. in Biomedical Informatics and Applied Statistics, Master’s and Bachelor’s degrees in Computer Science.
Anjali Shah, PhD
Senior Data Scientist | IBM