Linear Regression model from Scratch | Deep Learning with PyTorch

Linear Regression Implementation with PyTorch | Full Deep Learning Tutorial - Beginner to advanced.
Complete "Deep Learning with PyTorch" Playlist : https://www.youtube.com/playlist?list=PLz6pthWWCdfRMjkzgzOVJZqVhvLk1gj9Q

🚀 Key features of this series :
- Watch live hands-on tutorials on YouTube
- Train models using Google Colab on GCP for free!
- Build an end-to-end real-world course project

2.1. Linear Regression Implementation
In this tutorial, we implement linear regression model from scratch using PyTorch tensors. We also conduct optimization process (Gradient Descent) to learn the model parameters (weights and biases).

🎯 Topics covered in this video:
⌨️ Introduction, NumPy arrays vs Tensors.
⌨️ Intro. to sample data that we are considering. Neural network architecture for linear regression model.
⌨️ Linear regression implementation - forward and backward pass.
⌨️ Gradient descent process
⌨️ Putting it all-together. Conduct the whole training process.
⌨️ Conclusion and motivation for creating a MLP model.

Time Breaks:
00:00 Introduction, NumPy arrays vs Tensors.
01:34 Dataset, NN architecture for linear regression.
05:31 Linear regression, gradient descent implementation.
16:14 Putting it all-together.
19:32 Conclusion and next steps.

Resources:
🔗 Code : https://github.com/mohangollapalli/dl-pytorch/blob/f4c183949ccbc4d9dc7d8f9c95deb3ea8f3e469c/02-regression.ipynb

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Видео Linear Regression model from Scratch | Deep Learning with PyTorch канала Simplified AI Course