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Intro to Deep Learning Shortlist

Hardik Sahi, Sandra Lopez, Amir Parizi, Nour FahmyTotal time needed: ~3 hours
Learning Objectives
Learn the fundamental structures of deep learning and the mathematical functions that help them implement deep learning.
Potential Use Cases
If you're a beginner looking to figure out where to begin, or a trained vet hoping to review some basics.
Target Audience
BEGINNERIf you're a beginner looking to figure out where to begin, or a trained vet hoping to review some basics.
Go through the following annotated items in order:
OTHER 1. Hidden layers
  • What are the layers in a neural network and what do they do?
50 minutes
OTHER 2. Activation functions
  • What is an activation function and why is it important in the context of NNs?
20 minutes
ARTICLE 3. Backpropagation
  • What is the most common technique to travel through neural networks and how do we update our weights of our NN?
60 minutes
OTHER 4. Gradient Descent
  • What is the most common optimization technique?
30 minutes
ARTICLE 5. L1 Regularization
  • How do we prevent overfitting of our weights?
10 minutes
ARTICLE 6. L2 Regularization
  • How do we prevent overfitting of our weights?
10 minutes

Concepts Convered