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publicShareStarIntro to Deep Learning Shortlist
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Total 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 Covered