---
title: "Visualizing Gradient Descent Parameters in Torch"
url: https://daily.dev/posts/visualizing-gradient-descent-parameters-in-torch-keyntopwr
source_url: https://towardsdatascience.com/visualizing-gradient-descent-parameters-in-torch-332a63d1e5c5
type: article
source: "Medium"
published: 2024-02-27T04:03:41.434Z
updated: 2024-05-09T09:28:18.744Z
tags: ["machine-learning", "python", "pytorch", "gradient-descent"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

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# Visualizing Gradient Descent Parameters in Torch

**[Medium](https://daily.dev/sources/medium_js)** · 7 min read · 0 upvotes · 0 comments

## Summary

This post explores the effects of different parameters on model training using Torch's stochastic gradient descent optimizer. It includes a toy problem of performing linear regression, visualizations of loss functions, and the impact of parameters like momentum, weight decay, dampening, and nesterov momentum.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/visualizing-gradient-descent-parameters-in-torch-332a63d1e5c5>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#pytorch](https://daily.dev/tags/pytorch), [#gradient-descent](https://daily.dev/tags/gradient-descent)

[View this post on daily.dev](https://daily.dev/posts/visualizing-gradient-descent-parameters-in-torch-keyntopwr)