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Estimated time needed to finish: 6 minutes
Questions this item addresses:
  • Why to slowly reduce your learning rate?
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Why is this important?

Play with learning rates during training could help to accelerate all the training process.

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Author(s) / creator(s) / reference(s)
Andrew NG
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Deep Learning Model Training And Optimization

Total time needed: ~6 hours
With this recipe you will understand how to train a neural network
Potential Use Cases
Build your own Neural Network
Who is This For ?
Click on each of the following annotated items to see details.
VIDEO 1. Loss Functions
  • What is the relevance of loss functions for deep learning?
17 minutes
VIDEO 2. Optimization using Gradient Descent - Part 1
  • How do neural networks learn?
26 minutes
VIDEO 3. Optimization using Gradient Descent - Part 2
  • How do you use gradient descent for parameter updating?
17 minutes
VIDEO 4. Chain Rule, Backpropagation & Autograd
  • How else can I train my neural nets?
21 minutes
REPO 5. Hands-on Optimization
  • How to implement optimization methods in PyTorch?
  • How can we update parameters with Gradient Descent?
  • How to implement gradient descent in Pytorch?
30 minutes
RECIPE 6. Understanding BackPropagation
4 hours
VIDEO 7. Learning Rate Decay
  • Why to slowly reduce your learning rate?
6 minutes
VIDEO 8. Weights Initialization
  • Why to initialize parameters for DNN?
6 minutes

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