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UofT-DSI | Deep Learning - Assignment 1 #86

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1f17514
Update Jupyter notebook metadata: change display name to 'DSI_partici…
ajharris Oct 23, 2025
5c2de6c
Add introductory modules on Deep Learning, Neural Networks, and Recom…
ajharris Oct 24, 2025
1bf9c0e
Implement feature X to enhance user experience and fix bug Y in module Z
ajharris Oct 25, 2025
41280a2
Update lab_2 notebook execution counts and add identity matrix one-ho…
ajharris Oct 25, 2025
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976 changes: 976 additions & 0 deletions 01_materials/labs/.ipynb_checkpoints/lab_1-checkpoint.ipynb
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27 changes: 27 additions & 0 deletions 01_materials/labs/lab1test.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,27 @@
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["TF_NUM_INTRAOP_THREADS"] = "1"
os.environ["TF_NUM_INTEROP_THREADS"] = "1"

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()
X = digits.data
y = to_categorical(digits.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = Sequential([
Dense(64, activation="relu", input_shape=(64,)),
Dense(10, activation="softmax")
])
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])

print("Starting training…")
model.fit(X_train, y_train, epochs=2, batch_size=32, validation_split=0.2)
print("Done")
1,117 changes: 1,002 additions & 115 deletions 01_materials/labs/lab_1.ipynb
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147 changes: 121 additions & 26 deletions 01_materials/labs/lab_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -36,9 +36,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 300x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_index = 45\n",
"plt.figure(figsize=(3, 3))\n",
Expand All @@ -58,7 +69,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -91,7 +102,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -101,18 +112,43 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_hot(n_classes=10, y=3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
" [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_hot(n_classes=10, y=[0, 4, 9, 1])"
]
Expand Down Expand Up @@ -143,7 +179,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {
"collapsed": false
},
Expand All @@ -164,9 +200,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n"
]
}
],
"source": [
"print(softmax([10, 2, -3]))"
]
Expand All @@ -181,9 +225,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n",
" [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n"
]
}
],
"source": [
"X = np.array([[10, 2, -3],\n",
" [-1, 5, -20]])\n",
Expand All @@ -199,18 +252,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"print(np.sum(softmax([10, 2, -3])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"softmax of 2 vectors:\n",
"[[9.99662391e-01 3.35349373e-04 2.25956630e-06]\n",
" [2.47262316e-03 9.97527377e-01 1.38536042e-11]]\n"
]
}
],
"source": [
"print(\"softmax of 2 vectors:\")\n",
"X = np.array([[10, 2, -3],\n",
Expand All @@ -227,9 +298,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1.]\n"
]
}
],
"source": [
"print(np.sum(softmax(X), axis=1))"
]
Expand All @@ -251,9 +330,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.01005033585350145\n"
]
}
],
"source": [
"def nll(Y_true, Y_pred):\n",
" Y_true = np.asarray(Y_true)\n",
Expand All @@ -279,9 +366,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.605170185988091\n"
]
}
],
"source": [
"print(nll([1, 0, 0], [0.01, 0.01, .98]))"
]
Expand Down Expand Up @@ -822,7 +917,7 @@
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"display_name": "DSI_participant",
"language": "python",
"name": "python3"
},
Expand All @@ -836,7 +931,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.12"
"version": "3.10.17"
}
},
"nbformat": 4,
Expand Down
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