Linear Regression Implementation From Scratch
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The document outlines the implementation of linear regression from scratch, detailing steps such as data generation, visualization, model parameter definition, loss function establishment, and optimization algorithm application. It includes an example data set and prints a small batch for illustration. The text aims to help readers understand the fundamentals of creating a linear regression model.
![Print a Small Data Batch
[[ 1.7130351 2.0944266 ] [-1.95122 -1.0635319 ]
[ 0.22546557 0.8499169 ] [-1.6903632 -0.15907401]
[ 0.85316896 1.0389048 ] [-0.17798574 1.410267
[-2.152955 -1.3841926 ] [ 0.8605494 -0.22276537]
[-0.33177793 -0.99060804] [ 2.584889 -1.1416703 ]]
<NDArray 10x2 @cpu(0)>
[ 0.5036066 3.9271483 1.7579273 1.353747 2.3738737 –
4.593405 6.6843915 6.8792953 13.258079 ]
NDArray 10 @cpu(0)>](https://image.slidesharecdn.com/3-190927082354/85/Linear-Regression-Implementation-From-Scratch-8-320.jpg)
Linear Regression Implementation From Scratch
- 1. Linear Regression Implementation from Scratch Eng Teong Cheah
- 2. Contents Linear Regression Implementation from Scratch
- 4. Linear Regression Implementation from Scratch
- 6. Visualize the Second Feature and Label
- 7. Reading Data
- 8. Print a Small Data Batch [[ 1.7130351 2.0944266 ] [-1.95122 -1.0635319 ] [ 0.22546557 0.8499169 ] [-1.6903632 -0.15907401] [ 0.85316896 1.0389048 ] [-0.17798574 1.410267 [-2.152955 -1.3841926 ] [ 0.8605494 -0.22276537] [-0.33177793 -0.99060804] [ 2.584889 -1.1416703 ]] <NDArray 10x2 @cpu(0)> [ 0.5036066 3.9271483 1.7579273 1.353747 2.3738737 – 4.593405 6.6843915 6.8792953 13.258079 ] NDArray 10 @cpu(0)>
- 10. Define the Linear Model
- 11. Define the Loss function
- 12. Define the Optimization Algorithm
- 13. Training
- 14. Training
- 15. Thanks! Does anyone have any questions? Twitter: @walkercet Blog: https://ceteongvanness.wordpress.com
- 16. Resources Dive into Deep Learning