Optimization, Machine Learning Models, and TensorFlow (Part 2 of 4)

From the creator

This is Part 2 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1 [https://channel9.msdn.com/Shows/AI-Show/An-Intuitive-Approach-to-Machine-Learning-Models-Part-1-of-4] was all about the foundational concepts of machine learning. In this part I get into more advanced machine learning concepts. These include: [00:13] Optimization (I explain calculus!!!) [04:40] Gradient descent [06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more) [07:04] Neural Networks (as an extension to linear models) [09:28] Brief Review of TensorFlow Hope you enjoy Part 2! As always feel free to send any feedback or add any comments below if you have any questions. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show: https://aka.ms/aishowsubscribe Create a Free account (Azure): https://aka.ms/aishow-seth-azurefree Deep Learning vs. Machine Learning: https://aka.ms/AIShow/DLvML Get Started with Machine Learning: https://aka.ms/AIShow/StartML Follow: https://twitter.com/sethjuarez Follow: https://twitter.com/ch9 Follow: https://twitter.com/Azure Follow: https://twitter.com/msdev

Published 2020-02-18 · Watch on YouTube ↗
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