Over the past three months, we’ve seen how experienced engineers working in software, medicine, physics, child development and other fields can become machine learning practitioners with our combination of educational resources and mentorship.
Fine-Tuning GPT-2 Small for Question Answering
Using Intrinsic Motivation to Solve Robotic Tasks with Sparse Rewards
CREATURE: Human Learning Powered by Machine learning
Reinforcement Learning for Medical Applications
Helen (Mengxin) Ji
Sentiment Analysis Using Reinforcement Learning
Exploring Gamma: Discount of the Future, or Weight of the Past
Visualizing & Evaluating Image Synthesis GANs using the Techniques of Activation Atlases
Knowledge Distillation For Transformer Language Models
Our Scholars demonstrate core technical skills across various expert domains and self-motivation—critical competences for a self-directed program like this one. They each entered the field of machine learning as relative newcomers, and we hope their progress shows how accessible machine learning is. To begin your learning journey, check out some of our educational materials. More information about the next class of Scholars and how to apply will be announced in July. Stay tuned!
Thanks to AWS for providing compute credits to the scholars. Additional thank you to our dedicated community mentors for their time advising the scholars on their projects.