Are you interested in: - quickly yet comprehensively learn-by-computational-training the math (linear algebra, probability, and statistics) principles behind deep learning models such as transformers and deep reinforcement learning models, and in gaining a solid intuitive understanding of how and why such models work in practice? - learning to code effectively using state-of-the-art tools (Tensorflow Functional & Sequential APIs, PyTorch, R tools such as MICE, GLMNET) applied to real-life data for your projects? I was a multiple-teaching-award-winning instructor at Stanford and currently a faculty of applied statistics working on several projects involving deep learning applications in industrial anomaly detection, computer vision, and survival analysis in sports. I'm also a certified TensorFlow Developer. I have helped several full- and part-time students from Stanford and the Bay Area industry to ace courses & projects at Stanford and to smoothly navigate the mathematical maze behind probability, statistics, optimization, linear algebra, and machine learning. Meeting sessions can be tailored to your specific background and needs. My rate is $50 per hour, and meetings will be via Google Meet. Please get in touch if interested.