Neural Networks Explained: The Perceptron Model 🧠

The beginning of introducing neural networks. In future videos, we'll discuss implementation using Keras & TensorFlow, optimization techniques, and deep learning. In this video, we motivate the structure of neural networks by starting with the perceptron model to model logical operators. We then proceed to add more complexity (multiple layers) and introduce the terminology and use cases of a neural network.

I mentioned for "homework" before the next Study Group on neural networks to watch this playlist from 3Blue1Brown: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

0:00 Overview structure of future Study Groups
1:27 Introducing neural networks
2:58 Perceptron model
5:20 Logic gates implementation
12:20 Multiple perceptrons: neural network
13:02 Hidden layer for latent features
16:56 Mathematical representation: Loss function
20:04 Relation to linear/polynomial regression?
22:43 TensorFlow playground: Visualizing learning process
27:40 Looking to next time
28:30 When deep learning is and isn't a solution
29:38 Analogy: Neural networks are children
30:30 Next time: Activation functions & hyperparameters
31:10 Homework for next time

Notebook(s) can be found on https://github.com/MrGeislinger/flatiron-school-data-science-curriculum-resources