About this Event
In Deep Learning Prerequisites, we went over the mathematical and computational foundations of Deep Learning.
In Deep Learning Part 2: Information Theory and Linear Models, we went over the theoretical foundations of Deep Learning.
In Deep Learning Part 3: Multilayer Perceptrons Done Right, what we learned in the first two sections payoff spectacularly when we are introduced to the most fundamental Deep Neural Network (DNN) architecture: the multilayer perceptron. We will discuss core properties of the multilayer perceptron, its implementations, how to select the best model configuration, and some of the key techniques that are used to train DNNs in industry today.
Recommended (but not required) prior reading:
1) Chapter 4 of Dive Into Deep Learning (excluding 4.9 and 4.10) - https://d2l.ai/chapter_multilayer-perceptrons/index.html
2) I also strongly recommend reading the "Evaluating and Comparing Estimators" section in the appendix of the free online textbook https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/statistics.html#evaluating-and-comparing-estimators
Special Considerations and Warnings
As in the previous 2 parts, I highly recommend attending the lecture with a laptop that has anaconda installed. You can find the installer at https://www.anaconda.com/products/individual
Also like in previous parts, I still strongly recommend that you try your best to read the recommended (but not required) prior reading material mentioned in the description.