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slides (lectures 1-9)
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slides/02- Regression models and linear prediction/ML4NuerScience_Linear_models.pptx
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slides/03- Probability and information theory/ML4NuerScience_Probability_info_theory.pptx
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slides/03- Probability and information theory/PDF_CDF.ipynb
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...Ensemble models and kernel-based models/ML4NuerScience_ensemble models_kernel_models.pptx
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import math\n", | ||
"\n", | ||
"def sigmoid(x):\n", | ||
" return 1 / (1 + math.exp(-x))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"0.5" | ||
] | ||
}, | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"sigmoid (-0)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import numpy as np\n", | ||
"\n", | ||
"x = np.arange (-5, 5, 1)\n", | ||
"y = np.zeros(10)\n", | ||
"for i in x:\n", | ||
" y[i] = sigmoid(x[i])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"[<matplotlib.lines.Line2D at 0x7f95f94e8460>]" | ||
] | ||
}, | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
}, | ||
{ | ||
"data": { | ||
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", | ||
"text/plain": [ | ||
"<Figure size 432x288 with 1 Axes>" | ||
] | ||
}, | ||
"metadata": { | ||
"needs_background": "light" | ||
}, | ||
"output_type": "display_data" | ||
} | ||
], | ||
"source": [ | ||
"import matplotlib.pyplot as plt\n", | ||
"import seaborn as sns\n", | ||
"plt.xticks(x)\n", | ||
"plt.plot(x,y)" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "sandbox", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.8.12" | ||
}, | ||
"orig_nbformat": 4, | ||
"vscode": { | ||
"interpreter": { | ||
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} |
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