Machine Learning: From ABCs to DEFs

Explore the fundamentals of machine learning in this one-hour conference talk. Delve into the "what"s, "why"s, and "how"s of various machine learning problems, accompanied by code examples. Learn about key concepts such as stochastic gradient descent and cross entropy, while focusing on the essential components of people, data, and code. Gain practical insights into neural networks, mean squared error, and transfer learning. Discover how to apply machine learning techniques to real-world scenarios, including wine reviews. By the end of the talk, acquire the knowledge to build simple AI models and understand the probabilistic nature of machine learning outcomes.

Introduction
Groot
Neural Network
Mean squared error
Intuition
Whiteboard
Loss
Tools
Google Cloud
Transfer Learning
Inference
Running things
Wine reviews
Questions