Dive into Deep Learning
The Dive into Deep Learning study sessions aim to provide code-focused sessions by reimplementing selected models from the book Dive into Deep Learning.
These sessions are for engineers and researchers interested in implementing models from scratch. We hope to help participants either get started in their Machine Learning journey or deepen their knowledge if they already have previous experience by
Helping participants to create their models end-to-end by reimplementing models from scratch and discussing what modules/elements need to be included (e.g., data preprocessing, dataset generation, data transformation, etc.) to train an ML model.
Discussing and resolving coding questions participants might have during the sessions.
All sessions are available on GitHub (code) and on YouTube (tutorials / code walkthroughs) and were led by the MLT core contributors.
Pierre Wüthrich is an AI Research Engineer at Elix Inc. focusing on drug discovery and material informatics. Before joining Elix, he gained experience in the field of applied reinforcement learning at another startup company specialized in machine automation through machine learning. [LI] [Twitter]
Mrityunjay Bhardwaj is the Head of AI at Jupiter AI Labs which focuses on providing research-oriented enterprise-grade ML Solutions. Apart from that, he is also trying his hand in ML research. [Twitter]
Americas / EMEA sessions
Devansh Agarwal is a Data Scientist at BMS. He graduated with a Ph.D. in Astronomy where he has developed pipelines using high-performance computing and machine learning to aid the discovery of astronomical objects. [LI] [Twitter]
Kshitij Aggarwal is a 4th-year graduate student at the Department of Physics and Astronomy at West Virginia University. He uses data analysis, machine learning, and high-performance computing to discover and study a new class of astronomical objects called Fast Radio Bursts. [LI] [Twitter]