From 4a08818706f7d32ca22de5ce7fee833f5c503b2c Mon Sep 17 00:00:00 2001
From: udlbook <110402648+udlbook@users.noreply.github.com>
Date: Wed, 5 Nov 2025 09:48:13 -0500
Subject: [PATCH] Created using Colab

---
 Notebooks/Chap06/6_2_Gradient_Descent.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Notebooks/Chap06/6_2_Gradient_Descent.ipynb b/Notebooks/Chap06/6_2_Gradient_Descent.ipynb
index 1969faa..08731db 100644
--- a/Notebooks/Chap06/6_2_Gradient_Descent.ipynb
+++ b/Notebooks/Chap06/6_2_Gradient_Descent.ipynb
@@ -301,7 +301,7 @@
       "source": [
         "def loss_function_1D(dist_prop, data, model, phi_start, search_direction):\n",
         "  # Return the loss after moving this far\n",
-        "  return compute_loss(data[0,:], data[1,:], model, phi_start+ search_direction * dist_prop)\n",
+        "  return compute_loss(data[0,:], data[1,:], model, phi_start - search_direction * dist_prop)\n",
         "\n",
         "def line_search(data, model, phi, gradient, thresh=.00001, max_dist = 0.1, max_iter = 15, verbose=False):\n",
         "    # Initialize four points along the range we are going to search\n",
@@ -365,7 +365,7 @@
         "def gradient_descent_step(phi, data,  model):\n",
         "  # TODO -- update Phi with the gradient descent step (equation 6.3)\n",
         "  # 1. Compute the gradient (you wrote this function above)\n",
-        "  # 2. Find the best step size alpha using line search function (above) -- use negative gradient as going downhill\n",
+        "  # 2. Find the best step size alpha using line search function (above)\n",
         "  # 3. Update the parameters phi based on the gradient and the step size alpha.\n",
         "\n",
         "  return phi"