update notebooks with latest changes, rm notebooks with no code

carpentries-incubator · Nov 27, 2024 · 9aee108 · 9aee108
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commit 9aee108
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diff --git a/code/3-model-fairness-deep-dive.ipynb b/code/3-model-fairness-deep-dive.ipynb
diff --git a/code/5a-explainable-AI-method-overview.ipynb b/code/5a-explainable-AI-method-overview.ipynb
diff --git a/code/5b-deep-dive-into-methods.ipynb b/code/5b-deep-dive-into-methods.ipynb
diff --git a/code/5c-probes.ipynb b/code/5c-probes.ipynb
@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d098f24e",
+   "id": "8f22377e",
    "metadata": {},
    "source": [
     "# Explainability methods: linear probe"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b870b01b",
+   "id": "3f7a541d",
    "metadata": {},
    "source": [
     ":::::::::::::::::::::::::::::::::::::: questions \n",
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5244974",
+   "id": "bdd101f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9b6474b",
+   "id": "b01bfbad",
    "metadata": {},
    "source": [
     "Now, let's set the random seed to ensure reproducibility. Setting random seeds is like setting a starting point for your machine learning adventure. It ensures that every time you train your model, it starts from the same place, using the same random numbers, making your results consistent and comparable."
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8a61c064",
+   "id": "3bfcd804",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069a82ba",
+   "id": "d80250f6",
    "metadata": {},
    "source": [
     "##### Loading the Dataset\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe8ca9a8",
+   "id": "8eb08128",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb2f2751",
+   "id": "fd768987",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc2bd075",
+   "id": "9c63eb60",
    "metadata": {},
    "source": [
     "##### Loading the Model\n",
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "efde1f9d",
+   "id": "6fc272aa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9825c2a2",
+   "id": "46aad0a5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7859a2c2",
+   "id": "7a5f2539",
    "metadata": {},
    "source": [
     "Let's see what the model's architecture looks like. How many layers does it have?"
@@ -174,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3f4c2beb",
+   "id": "53e9764a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02d3317f",
+   "id": "c895dae9",
    "metadata": {},
    "source": [
     "Let's see if your answer matches the actual number of layers in the model."
@@ -192,7 +192,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7474a7a2",
+   "id": "69e55967",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38c22a12",
+   "id": "cdef0b95",
    "metadata": {},
    "source": [
     "##### Setting up the Probe\n",
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8205c177",
+   "id": "56f142de",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ec07bb2e",
+   "id": "cc2a7a6b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e132be2c",
+   "id": "ade6219c",
    "metadata": {},
    "source": [
     "Now, it's finally time to define our probe! We set this up as a class, where the probe itself is an object of this class. \n",
@@ -318,7 +318,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78a2d7f4",
+   "id": "0ce14e70",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d367d0f4",
+   "id": "c1a7d0dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5baabd5a",
+   "id": "b7b861b5",
    "metadata": {},
    "source": [
     "##### Analysing the model using Probes\n",
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "027d56ad",
+   "id": "e8b16cb4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ea3b0248",
+   "id": "65c7f01d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,7 +544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47b8508b",
+   "id": "78c7faca",
    "metadata": {},
    "source": [
     "Which layer has the best accuracy? What does this tell us about the model?\n",
@@ -557,7 +557,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "85d8720c",
+   "id": "b1bc5534",
    "metadata": {},
    "outputs": [],
    "source": [