What I Learned Predicting Crypto Flows with Deep Learning and Blockchain
March 25 @ 7:00 pm - 9:00 pm
6:30 arrive, register, pizza, network
7:00 ACM intro
7:10 to 8:30-ish talk
7:10 Live stream Click the link to set your PC up and get ready
Deep learning can seem like a dark art. The reality is that it is very achievable to get a model working and predicting well. But deep learning also has some myths and pitfalls of which you should be aware. I’ll walk through a project I did to predict cryptocurrency flows using deep learning and blockchain data. This includes code snippets and real world results with various models side by side. I’ll also address myths and preconceptions about deep learning to help you dig in for your own projects.
Michelle is passionate about solving big impactful problems with technology, powered by big data and artificial intelligence. A dynamic hands-on leader, her experience spans data science, engineering, and product management with a track record of success in delivering commercial software products globally at scale.
She has successfully run multinational technical teams of 25+ for one of largest tech cos in the world (Oracle), as well as 3 companies which were acquired. Brought products to global market leadership. Shipped technology in more than 25 countries and 30 languages. She is currently CEO/Co-Founder at Data Simply (DataSimply.com) which uses data science to help people invest inline with their beliefs and values (environmental-social-governance), by mining text in company disclosures. There she developed complex real-time data pipelines that have powered more than 100 million automated investment insights. Quantitative Masters degree (MBA, Finance, Kellogg) and hands on AI/machine learning engineer.