Lesson 2 In-Class Discussion ✅

ste · November 1, 2018, 9:12pm

I’ve extended the sgd notebook of lesson 2 to polynomial fitting:

That wasn’t easy and I was stuck with an error on the “update” function…
But solving it, I 've got a better understanding of PyTorch mechanics: in particular the difference between constant tensors (without gradient) and parameters (differentiable - with “gradient”).

github.com

artste/fastai-samples/blob/master/dl1/lesson2-sgd-polynomial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Linear-Regression-problem\" data-toc-modified-id=\"Linear-Regression-problem-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Linear Regression problem</a></div><div class=\"lev1 toc-item\"><a href=\"#Gradient-Descent\" data-toc-modified-id=\"Gradient-Descent-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Gradient Descent</a></div><div class=\"lev1 toc-item\"><a href=\"#Gradient-Descent---Classification\" data-toc-modified-id=\"Gradient-Descent---Classification-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Gradient Descent - Classification</a></div><div class=\"lev1 toc-item\"><a href=\"#Gradient-descent-with-numpy\" data-toc-modified-id=\"Gradient-descent-with-numpy-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Gradient descent with numpy</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from fastai import *"
   ]
  },

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