Updated comments to make clearer.

Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb

@@ -1,18 +1,16 @@
 {
   "cells": [
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/udlbook/udlbook/blob/main/Notebooks/Chap03/3_1_Shallow_Networks_I.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1Z6LB4Ybn1oN"
@@ -42,7 +40,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "wQDy9UzXpnf5"
@@ -102,8 +99,8 @@
       "source": [
         "# Define a shallow neural network with, one input, one output, and three hidden units\n",
         "def shallow_1_1_3(x, activation_fn, phi_0,phi_1,phi_2,phi_3, theta_10, theta_11, theta_20, theta_21, theta_30, theta_31):\n",
-        "  # TODO Replace the lines below to compute the three initial lines\n",
-        "  # (figure 3.3a-c) from the theta parameters.  These are the preactivations\n",
+        "  # TODO Replace the code below to compute the three initial lines\n",
+        "  # from the theta parameters (i.e. implement equations at bottom of figure 3.3a-c).  These are the preactivations\n",
         "  pre_1 = np.zeros_like(x)\n",
         "  pre_2 = np.zeros_like(x)\n",
         "  pre_3 = np.zeros_like(x)\n",
@@ -199,7 +196,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "T34bszToImKQ"
@@ -210,7 +206,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "jhaBSS8oIWSX"
@@ -269,7 +264,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "osonHsEqVp2I"
@@ -354,9 +348,8 @@
   ],
   "metadata": {
     "colab": {
-      "authorship_tag": "ABX9TyPBNztJrxnUt1ELWfm1Awa3",
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "Python 3",