Covers: theory of Optimization
Estimated time needed to finish: 26 minutes
Questions this item addresses:
  • How do neural networks learn?
How to use this item?

In this video you will learn the basics of gradient decent and how it can be used to train neural nets.

  • Gradient Descent is one of the most used optimization methods to fit our neural networks. It's important because it allows us to calculate the weigths by updating them with the gradients.

Alt text

In regular Deep Learning Framework you have to define your Optimizer. There are many ways to do this, but first you have to learn the equation that involves weights and gradients.

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Author(s) / creator(s) / reference(s)
Amir Hajian
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Deep Learning Model Training And Optimization

Total time needed: ~6 hours
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

Concepts Covered

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