Covers: implementation of Optimization

- How do you use gradient descent for parameter updating?

In this video you will implement gradient descent and get more familiar with the concept of derivatives and walk through a training example.

Fail to play? Open the link directly: https://youtu.be/x7F7zZd23PU

Amir Hajian

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- Objectives
- With this recipe you will understand how to train a neural network
- Potential Use Cases
- Build your own Neural Network
- Who is This For ?
- BEGINNER

Click on each of the following **annotated items** to see details.

Resources5/8

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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