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# Assignment 1
The goal of this assignment is to supply you with machine learning models and algorithms. In this notebook, we will cover linear and nonlinear models, the concept of loss functions and some optimization techniques. All mathematical operations should be implemented in **NumPy** only.
## Table of conte... | 573a6d2178c0300d5138735a1933012506c0e4c7 | 48,702 | ipynb | Jupyter Notebook | week_2/ML.ipynb | shoemaker9/aml2019 | f09c3ac942158b6fe9748c76552d6ace73f47815 | [
"MIT"
] | null | null | null | week_2/ML.ipynb | shoemaker9/aml2019 | f09c3ac942158b6fe9748c76552d6ace73f47815 | [
"MIT"
] | null | null | null | week_2/ML.ipynb | shoemaker9/aml2019 | f09c3ac942158b6fe9748c76552d6ace73f47815 | [
"MIT"
] | null | null | null | 35.012221 | 483 | 0.544618 | true | 9,195 | Qwen/Qwen-72B | 1. YES
2. YES | 0.91848 | 0.90599 | 0.832134 | __label__eng_Latn | 0.962431 | 0.771659 |
# Propriedade da multiplicação e janelas
Imagine que você tenha um fenômeno (ou sinal) com duração infinita, $x(t)$, que deseja observar (medir).
Quando medimos $x(t)$ por um tempo finito estamos observando o fenômeno por uma janela temporal $w(t)$ finita. Na prática, o sinal observado é:
\begin{equation}
x_o(t) = x... | 8638d2e64a8902d181a010bc4b46cc7bc8903952 | 251,592 | ipynb | Jupyter Notebook | Aula 27 - propriedade da multiplicacao e janelas/Jamelas.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | 8 | 2020-10-01T20:59:33.000Z | 2021-07-27T22:46:58.000Z | Aula 27 - propriedade da multiplicacao e janelas/Jamelas.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | null | null | null | Aula 27 - propriedade da multiplicacao e janelas/Jamelas.ipynb | RicardoGMSilveira/codes_proc_de_sinais | e6a44d6322f95be3ac288c6f1bc4f7cfeb481ac0 | [
"CC0-1.0"
] | 9 | 2020-10-15T12:08:22.000Z | 2021-04-12T12:26:53.000Z | 852.854237 | 62,988 | 0.947379 | true | 1,540 | Qwen/Qwen-72B | 1. YES
2. YES | 0.899121 | 0.782662 | 0.703709 | __label__por_Latn | 0.357245 | 0.473282 |
# Scenario A - Noise Level Variation (results evaluation)
This file is used to evaluate the inference (numerical) results.
The model used in the inference of the parameters is formulated as follows:
\begin{equation}
\large y = f(x) = \sum\limits_{m=1}^M \big[A_m \cdot e^{-\frac{(x-\mu_m)^2}{2\cdot\sigma_m^2}}\big] ... | f5a18e8a02c6ac6cbc913c88e7a82adcaa1fa6e5 | 21,680 | ipynb | Jupyter Notebook | code/scenarios/scenario_a/scenario_noise_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | 1 | 2021-01-07T02:22:25.000Z | 2021-01-07T02:22:25.000Z | code/scenarios/scenario_a/scenario_noise_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | code/scenarios/scenario_a/scenario_noise_evaluation.ipynb | jnispen/PPSDA | 910261551dd08768a72ab0a3e81bd73c706a143a | [
"MIT"
] | null | null | null | 32.118519 | 130 | 0.344557 | true | 4,410 | Qwen/Qwen-72B | 1. YES
2. YES | 0.787931 | 0.640636 | 0.504777 | __label__kor_Hang | 0.132317 | 0.011095 |
<p align="center">
</p>
# Data Science Basics in Python Series
## Chapter VI: Basic Statistical Analysis in Python
### Michael Pyrcz, Associate Professor, The University of Texas at Austin
*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*
#### Basic Univariate Statistics
Her... | b5d7f6a8b369fe3693af97ff7fd2a54fa6bf07ca | 117,620 | ipynb | Jupyter Notebook | PythonDataBasics_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 403 | 2017-10-15T02:07:38.000Z | 2022-03-30T15:27:14.000Z | PythonDataBasics_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 4 | 2019-08-21T10:35:09.000Z | 2021-02-04T04:57:13.000Z | PythonDataBasics_Statistics.ipynb | caf3676/PythonNumericalDemos | 206a3d876f79e137af88b85ba98aff171e8d8e06 | [
"MIT"
] | 276 | 2018-06-27T11:20:30.000Z | 2022-03-25T16:04:24.000Z | 97.126342 | 28,384 | 0.835122 | true | 5,816 | Qwen/Qwen-72B | 1. YES
2. YES | 0.894789 | 0.867036 | 0.775814 | __label__eng_Latn | 0.848654 | 0.640809 |
# Teoría del Error
<p><code>Python en Jupyter Notebook</code></p>
<p>Creado por <code>Giancarlo Ortiz</code> para el curso de <code>Métodos Numéricos</code></p>
<style type="text/css">
.formula {
background: #f7f7f7;
border-radius: 50px;
padding: 15px;
}
.border {
display: in... | bc069757e7b6c8e04c5e8f232974178bf77ed324 | 21,322 | ipynb | Jupyter Notebook | Jupyter/13_Error.ipynb | GiancarloBenavides/Metodos-Numericos | c35eb538d33b8dd58eacccf9e8b9b59c605d7dba | [
"MIT"
] | 1 | 2020-10-29T19:13:39.000Z | 2020-10-29T19:13:39.000Z | Jupyter/13_Error.ipynb | GiancarloBenavides/Metodos-Numericos | c35eb538d33b8dd58eacccf9e8b9b59c605d7dba | [
"MIT"
] | null | null | null | Jupyter/13_Error.ipynb | GiancarloBenavides/Metodos-Numericos | c35eb538d33b8dd58eacccf9e8b9b59c605d7dba | [
"MIT"
] | 1 | 2020-11-12T20:22:40.000Z | 2020-11-12T20:22:40.000Z | 54.392857 | 2,040 | 0.408498 | true | 5,511 | Qwen/Qwen-72B | 1. YES
2. YES | 0.651355 | 0.845942 | 0.551009 | __label__spa_Latn | 0.31264 | 0.118507 |
# Introduction to Quantum Physics
### A complex Number
$c = a + ib$
Acircle of radius 1: $e^{-i\theta}$
### Single Qubit System ($\mathcal{C}^{2}$ -space)
$|\psi \rangle = \alpha |0 \rangle + \beta | 1 \rangle $
$ \langle \psi | \psi \rangle = 1 \implies \alpha^{2} + \beta^{2} = 1 $
- Operators are 2 by 2 matri... | 70ae0de7fab9092ebcbb172f36dcdc1fd3ab01e3 | 13,210 | ipynb | Jupyter Notebook | day1/3. Quantum-Physics-of-Quantum-Computing.ipynb | srh-dhu/Quantum-Computing-2021 | 5d6f99776f10224df237a2fadded25f63f5032c3 | [
"MIT"
] | 12 | 2021-07-23T13:38:20.000Z | 2021-09-07T00:40:09.000Z | day1/3. Quantum-Physics-of-Quantum-Computing.ipynb | Pratha-Me/Quantum-Computing-2021 | bd9cf9a1165a47c61f9277126f4df04ae5562d61 | [
"MIT"
] | 3 | 2021-07-31T08:43:38.000Z | 2021-07-31T08:43:38.000Z | day1/3. Quantum-Physics-of-Quantum-Computing.ipynb | Pratha-Me/Quantum-Computing-2021 | bd9cf9a1165a47c61f9277126f4df04ae5562d61 | [
"MIT"
] | 7 | 2021-07-24T06:14:36.000Z | 2021-07-29T22:02:12.000Z | 35.320856 | 449 | 0.533687 | true | 2,752 | Qwen/Qwen-72B | 1. YES
2. YES | 0.974821 | 0.853913 | 0.832412 | __label__eng_Latn | 0.95965 | 0.772306 |
# Math behind LinearExplainer with correlation feature perturbation
When we use `LinearExplainer(model, prior, feature_perturbation="correlation_dependent")` we do not use $E[f(x) \mid do(X_S = x_S)]$ to measure the impact of a set $S$ of features, but rather use $E[f(x) \mid X_S = x_s]$ under the assumption that the ... | b08d525728e387ab3ea393ea7bd2c82a80097ddb | 5,862 | ipynb | Jupyter Notebook | notebooks/linear_explainer/Math behind LinearExplainer with correlation feature perturbation.ipynb | santanaangel/shap | 1c1c4a45440f3475b8544251f9d9e5b43977cd0e | [
"MIT"
] | 16,097 | 2016-12-01T20:01:26.000Z | 2022-03-31T20:27:40.000Z | notebooks/linear_explainer/Math behind LinearExplainer with correlation feature perturbation.ipynb | santanaangel/shap | 1c1c4a45440f3475b8544251f9d9e5b43977cd0e | [
"MIT"
] | 2,217 | 2017-09-18T20:06:45.000Z | 2022-03-31T21:00:25.000Z | notebooks/linear_explainer/Math behind LinearExplainer with correlation feature perturbation.ipynb | santanaangel/shap | 1c1c4a45440f3475b8544251f9d9e5b43977cd0e | [
"MIT"
] | 2,634 | 2017-06-29T21:30:46.000Z | 2022-03-30T07:30:36.000Z | 48.446281 | 656 | 0.552712 | true | 1,662 | Qwen/Qwen-72B | 1. YES
2. YES | 0.909907 | 0.805632 | 0.73305 | __label__eng_Latn | 0.878589 | 0.541453 |
```python
!pip install pandas
import sympy as sym
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
sym.init_printing()
```
Requirement already satisfied: pandas in c:\users\usuario\.conda\envs\sys\lib\site-packages (1.1.2)
Requirement already satisfied: pytz>=2017.2 in... | d128ac18a21059fd9a2a7284a255d06e59cab6d8 | 232,579 | ipynb | Jupyter Notebook | .ipynb_checkpoints/4_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/4_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/4_Series_de_Fourier-checkpoint.ipynb | pierrediazp/Se-ales_y_Sistemas | b14bdaf814b0643589660078ddd39b5cdf86b659 | [
"MIT"
] | null | null | null | 74.30639 | 23,058 | 0.652587 | true | 3,748 | Qwen/Qwen-72B | 1. YES
2. YES | 0.824462 | 0.861538 | 0.710305 | __label__spa_Latn | 0.328317 | 0.488609 |
# Lecture 22: Transformations, Log-Normal, Convolutions, Proving Existence
## Stat 110, Prof. Joe Blitzstein, Harvard University
----
## Variance of Hypergeometric, con't
Returning to where we left off in Lecture 21, recall that we are considering $X \sim \operatorname{HGeom}(w, b, n)$ where $p = \frac{w}{w+b}$ an... | 992176428ba56397d6e1d82bacbeee1516152548 | 9,723 | ipynb | Jupyter Notebook | Lecture_22.ipynb | abhra-nilIITKgp/stats-110 | 258461cdfbdcf99de5b96bcf5b4af0dd98d48f85 | [
"BSD-3-Clause"
] | 113 | 2016-04-29T07:27:33.000Z | 2022-02-27T18:32:47.000Z | Lecture_22.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | null | null | null | Lecture_22.ipynb | snoop2head/stats-110 | 88d0cc56ede406a584f6ba46368e548010f2b14a | [
"BSD-3-Clause"
] | 65 | 2016-12-24T02:02:25.000Z | 2022-02-13T13:20:02.000Z | 38.583333 | 260 | 0.497275 | true | 2,366 | Qwen/Qwen-72B | 1. YES
2. YES | 0.808067 | 0.887205 | 0.716921 | __label__eng_Latn | 0.850217 | 0.503979 |
```python
import numpy as np
import sympy as sp
import pandas as pd
import math
import midterm as p1
import matplotlib.pyplot as plt
# Needed only in Jupyter to render properly in-notebook
%matplotlib inline
```
# Midterm
## Chinmai Raman
### 3/22/2016
$x_{n+1} = rx_n(1-x_n)$ for $x_0$ in $[0,1]$ and $r$ in $[2.... | 22fd6027671b5907b5a2ab82de098dc92883e97e | 322,677 | ipynb | Jupyter Notebook | Midterm.ipynb | ChinmaiRaman/phys227-midterm | c65a22052044799e34ec31d78827266629137636 | [
"MIT"
] | null | null | null | Midterm.ipynb | ChinmaiRaman/phys227-midterm | c65a22052044799e34ec31d78827266629137636 | [
"MIT"
] | null | null | null | Midterm.ipynb | ChinmaiRaman/phys227-midterm | c65a22052044799e34ec31d78827266629137636 | [
"MIT"
] | null | null | null | 340.017914 | 50,854 | 0.927373 | true | 4,051 | Qwen/Qwen-72B | 1. YES
2. YES | 0.841826 | 0.851953 | 0.717196 | __label__eng_Latn | 0.818317 | 0.504618 |
<table>
<tr align=left><td>
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
Note: This material largely follows the text "Numerical Linear Algebra" by Trefethen and Bau (SIAM, 1997) and is meant as... | 5398022bd9084116100bbca233ac4b37d4f1ad7f | 60,211 | ipynb | Jupyter Notebook | 10_LA_intro.ipynb | arkwave/intro-numerical-methods | e50f313b613d9f4aeb1ec6dd29a191bb771d092b | [
"CC-BY-4.0"
] | null | null | null | 10_LA_intro.ipynb | arkwave/intro-numerical-methods | e50f313b613d9f4aeb1ec6dd29a191bb771d092b | [
"CC-BY-4.0"
] | null | null | null | 10_LA_intro.ipynb | arkwave/intro-numerical-methods | e50f313b613d9f4aeb1ec6dd29a191bb771d092b | [
"CC-BY-4.0"
] | null | null | null | 33.92169 | 518 | 0.525336 | true | 13,197 | Qwen/Qwen-72B | 1. YES
2. YES | 0.715424 | 0.841826 | 0.602262 | __label__eng_Latn | 0.967854 | 0.237587 |
### Instructions
When running the notebook the first time, make sure to run all cells before making changes in the notebook. Hit Shift + Enter to run the selected cell or, in the top menu, click on: `Kernel` > `Restart Kernel and Run All Cells...` to rerun the whole notebook. If you make any changes in a cell, rerun t... | c73d3923c3a4c08ffd7d89c9ac0daf624fdf4447 | 87,609 | ipynb | Jupyter Notebook | binder/03_Measured_Data_Plotting.ipynb | villano-lab/galactic-spin-W1 | d95c706ccbc347f9bc61bb7c96b1314460bc2d0f | [
"CC-BY-4.0"
] | 1 | 2022-03-22T04:00:17.000Z | 2022-03-22T04:00:17.000Z | binder/03_Measured_Data_Plotting.ipynb | villano-lab/galactic-spin-W1 | d95c706ccbc347f9bc61bb7c96b1314460bc2d0f | [
"CC-BY-4.0"
] | 14 | 2021-11-05T18:17:19.000Z | 2022-02-19T20:35:05.000Z | binder/03_Measured_Data_Plotting.ipynb | villano-lab/galactic-spin-W1 | d95c706ccbc347f9bc61bb7c96b1314460bc2d0f | [
"CC-BY-4.0"
] | null | null | null | 286.303922 | 73,456 | 0.912406 | true | 3,378 | Qwen/Qwen-72B | 1. YES
2. YES | 0.930458 | 0.79053 | 0.735555 | __label__eng_Latn | 0.742484 | 0.547274 |
# Tarea Nro. 3 - LinAlg + Sympy
- Nombre y apellido: Ivo Andrés Astudillo
- Fecha: 26 de Noviembre de 2020
### Producto punto
```python
#from IPython.display import Image
#Image(filename='img/Tabla9.4.png')
```
5. La capacidad calorífica C<sub>p</sub> de un gas se puede modelar con la ecuación empí... | 4c3fb3a98502f98e20946a2df0c456877d99b4d1 | 1,905 | ipynb | Jupyter Notebook | Tareas/PrimerBimestre/TareaNro3 LinalgSympy/prueba.ipynb | iaastudillo/PythonIntermedio | 2b1393761445e7ee521d82c1dbb33826a9dd475e | [
"MIT"
] | null | null | null | Tareas/PrimerBimestre/TareaNro3 LinalgSympy/prueba.ipynb | iaastudillo/PythonIntermedio | 2b1393761445e7ee521d82c1dbb33826a9dd475e | [
"MIT"
] | null | null | null | Tareas/PrimerBimestre/TareaNro3 LinalgSympy/prueba.ipynb | iaastudillo/PythonIntermedio | 2b1393761445e7ee521d82c1dbb33826a9dd475e | [
"MIT"
] | null | null | null | 22.411765 | 223 | 0.545407 | true | 232 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.795658 | 0.689864 | __label__spa_Latn | 0.931601 | 0.441117 |
## подготовка:
```python
import numpy as np
from numpy.linalg import *
rg = matrix_rank
from IPython.display import display, Math, Latex, Markdown
from sympy import *
pr = lambda s: display(Markdown('$'+str(latex(s))+'$'))
def pmatrix(a, intro='',ending='',row=False):
if len(a.shape) > 2:
raise ValueE... | 129c5dc75e964d79c58737ff058df64fe79e03b4 | 13,031 | ipynb | Jupyter Notebook | 7ex/math.ipynb | TeamProgramming/ITYM | 1169db0238a74a2b2720b9539cb3bbcd16d0b380 | [
"MIT"
] | null | null | null | 7ex/math.ipynb | TeamProgramming/ITYM | 1169db0238a74a2b2720b9539cb3bbcd16d0b380 | [
"MIT"
] | null | null | null | 7ex/math.ipynb | TeamProgramming/ITYM | 1169db0238a74a2b2720b9539cb3bbcd16d0b380 | [
"MIT"
] | null | null | null | 21.017742 | 105 | 0.411097 | true | 1,816 | Qwen/Qwen-72B | 1. YES
2. YES | 0.849971 | 0.712232 | 0.605377 | __label__ast_Latn | 0.069041 | 0.244824 |
# Multiple Regression Analysis: OLS Asymptotics
So other than the finite sample properties in the previous chapters, we also need to know the ***asymptotic properties*** or ***large sample properties*** of estimators and test statistics. And fortunately, under the assumptions we have made, OLS has satisfactory large ... | 5728216be98958b6177646f60fec3e0c476338ab | 16,062 | ipynb | Jupyter Notebook | FinMath/Econometrics/Chap_05.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | 2 | 2018-11-27T10:31:08.000Z | 2019-01-20T03:11:58.000Z | FinMath/Econometrics/Chap_05.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | null | null | null | FinMath/Econometrics/Chap_05.ipynb | XavierOwen/Notes | d262a9103b29ee043aa198b475654aabd7a2818d | [
"MIT"
] | 1 | 2020-07-14T19:57:23.000Z | 2020-07-14T19:57:23.000Z | 59.932836 | 825 | 0.593201 | true | 4,245 | Qwen/Qwen-72B | 1. YES
2. YES | 0.63341 | 0.808067 | 0.511838 | __label__eng_Latn | 0.949282 | 0.0275 |
# Calculus
Contains an overview of calculus.
## Common derivatives
The following derivatives must simply be memorised:
$$
\begin{align}
{\Large \text{Very common}}\\
\\\
\frac{d}{dx} [x^n] =\ & n \cdot x^{n-1}\\
\\\
\frac{d}{dx} [e^x] =\ & e^x\\
\\\
\frac{d}{dx} [sin\ x] =\ & cos\ x\\
\\\
\frac{d}{dx} [cos\ x] =\ &... | 4fa26930190798de64f9c2ee84b92ca6fe8ebdab | 2,984 | ipynb | Jupyter Notebook | docs/mathematics/calculus.ipynb | JeppeKlitgaard/jepedia | c9af119a78b916bd01eb347a585ff6a6a0ca1782 | [
"MIT"
] | null | null | null | docs/mathematics/calculus.ipynb | JeppeKlitgaard/jepedia | c9af119a78b916bd01eb347a585ff6a6a0ca1782 | [
"MIT"
] | null | null | null | docs/mathematics/calculus.ipynb | JeppeKlitgaard/jepedia | c9af119a78b916bd01eb347a585ff6a6a0ca1782 | [
"MIT"
] | null | null | null | 26.642857 | 178 | 0.40315 | true | 670 | Qwen/Qwen-72B | 1. YES
2. YES | 0.953966 | 0.853913 | 0.814604 | __label__eng_Latn | 0.43476 | 0.73093 |
Lea cuidadosamente las siguientes **indicaciones** antes de comenzar el examen de prueba:
- Para resolver el examen edite este mismo archivo y renómbrelo de la siguiente manera: *Examen1_ApellidoNombre*, donde *ApellidoNombre* corresponde a su apellido paterno con la inicial en mayúscula, seguido de su primer nombre co... | f0eb08396ebf5cc5193bea3d46db8c8018509860 | 38,525 | ipynb | Jupyter Notebook | Modulo1/ProblemasAdicionales.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | Modulo1/ProblemasAdicionales.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | Modulo1/ProblemasAdicionales.ipynb | douglasparism/SimulacionM2018 | 85953efb86c7ebf2f398474608dfda18cb4cf5b8 | [
"MIT"
] | null | null | null | 133.767361 | 28,540 | 0.864581 | true | 1,884 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.92079 | 0.734962 | __label__spa_Latn | 0.984883 | 0.545895 |
# Tutorial
We will solve the following problem using a computer to assist with the technical aspects:
```{admonition} Problem
The matrix $A$ is given by $A=\begin{pmatrix}a & 1 & 1\\ 1 & a & 1\\ 1 & 1 & 2\end{pmatrix}$.
1. Find the determinant of $A$
2. Hence find the values of $a$ for which $A$ is singular.
3. For... | 55379c4199ba86e74f21c2ffed5a7996f9980b7e | 6,461 | ipynb | Jupyter Notebook | book/tools-for-mathematics/04-matrices/tutorial/.main.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 8 | 2020-09-24T21:02:41.000Z | 2020-10-14T08:37:21.000Z | book/tools-for-mathematics/04-matrices/tutorial/.main.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 87 | 2020-09-21T15:54:23.000Z | 2021-12-19T23:26:15.000Z | book/tools-for-mathematics/04-matrices/tutorial/.main.md.bcp.ipynb | daffidwilde/pfm | dcf38faccee3c212c8394c36f4c093a2916d283e | [
"MIT"
] | 3 | 2020-10-02T09:21:27.000Z | 2021-07-08T14:46:27.000Z | 21.608696 | 219 | 0.438322 | true | 872 | Qwen/Qwen-72B | 1. YES
2. YES | 0.974435 | 0.857768 | 0.835839 | __label__eng_Latn | 0.873963 | 0.780267 |
# Steinberg, Dave. Vibration Analysis for Electronic Equipment, 2nd ed., 1988
Steve Embleton | 20161116 | Notes
```python
%matplotlib inline
```
## Chapter 1, Introduction
Modes and vibrations basics. Designs for one input may fail when used in other areas with different forcing frequencies closer to the devices ... | 07e0b759b8847d3fdcfb7807416c012f0f913bed | 58,490 | ipynb | Jupyter Notebook | public/ipy/Steinberg_1988/Steinberg_1988.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | 1 | 2016-12-10T04:04:33.000Z | 2016-12-10T04:04:33.000Z | public/ipy/Steinberg_1988/Steinberg_1988.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | 3 | 2021-05-18T07:27:17.000Z | 2022-02-26T02:16:11.000Z | public/ipy/Steinberg_1988/Steinberg_1988.ipynb | stembl/stembl.github.io | 5108fc33dccd8c321e1840b62a4a493309a6eeff | [
"MIT"
] | null | null | null | 103.339223 | 39,330 | 0.825543 | true | 3,490 | Qwen/Qwen-72B | 1. YES
2. YES | 0.752013 | 0.774583 | 0.582496 | __label__eng_Latn | 0.984653 | 0.191664 |
(sec:LDs)=
# The Method of Lagrangian Descriptors
## Introduction
One of the biggest challenges of dynamical systems theory or nonlinear dynamics is the development of mathematical techniques that provide us with the capability of exploring transport in phase space. Since the early 1900, the idea of pursuing a quali... | 087b4e2278550820013ac56f3e65a1ee11b7b8c8 | 122,433 | ipynb | Jupyter Notebook | book/content/.ipynb_checkpoints/chapter3-checkpoint.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | 12 | 2020-07-24T17:35:42.000Z | 2021-08-12T17:31:53.000Z | book/_build/html/_sources/content/chapter3.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | 12 | 2020-05-26T17:28:38.000Z | 2020-07-27T10:40:54.000Z | book/content/chapter3.ipynb | champsproject/lagrangian_descriptors | b3a88a2243bd5b0dce7cc945f9504bfadc9a4b19 | [
"CC-BY-4.0"
] | null | null | null | 67.197036 | 2,195 | 0.638782 | true | 29,629 | Qwen/Qwen-72B | 1. YES
2. YES
| 0.689306 | 0.766294 | 0.528211 | __label__eng_Latn | 0.993579 | 0.065539 |
# Naive Bayes Classifier
Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features.
$
\begin{align}
\ P(h|D) = \frac{P(D|h) P(h)}{P(D)}
\end{align}
$
- P(h): the probability of hypothesis h being true (regardless of the data). This is known as the prior proba... | 278f3688830ec995075dbb045b2ef97cea569dbd | 9,740 | ipynb | Jupyter Notebook | classification/notebooks/08 - Naive Bayes.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | classification/notebooks/08 - Naive Bayes.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | classification/notebooks/08 - Naive Bayes.ipynb | pshn111/Machine-Learning-Package | fbbaa44daf5f0701ea77e5b62eb57ef822e40ab2 | [
"MIT"
] | null | null | null | 26.831956 | 129 | 0.424333 | true | 1,664 | Qwen/Qwen-72B | 1. YES
2. YES | 0.927363 | 0.831143 | 0.770772 | __label__eng_Latn | 0.540488 | 0.629093 |
+ This notebook is part of lecture 10 *The four fundamental subspaces* in the OCW MIT course 18.06 by Prof Gilbert Strang [1]
+ Created by me, Dr Juan H Klopper
+ Head of Acute Care Surgery
+ Groote Schuur Hospital
+ University Cape Town
+ <a href="mailto:juan.klopper@uct.ac.za">Email me with your thoug... | a60d8d81f72974bc37b93c9f424db1abe2ec204f | 13,257 | ipynb | Jupyter Notebook | _math/MIT_OCW_18_06_Linear_algebra/I_11_Subspaces.ipynb | aixpact/data-science | f04a54595fbc2d797918d450b979fd4c2eabac15 | [
"MIT"
] | 2 | 2020-07-22T23:12:39.000Z | 2020-07-25T02:30:48.000Z | _math/MIT_OCW_18_06_Linear_algebra/I_11_Subspaces.ipynb | aixpact/data-science | f04a54595fbc2d797918d450b979fd4c2eabac15 | [
"MIT"
] | null | null | null | _math/MIT_OCW_18_06_Linear_algebra/I_11_Subspaces.ipynb | aixpact/data-science | f04a54595fbc2d797918d450b979fd4c2eabac15 | [
"MIT"
] | null | null | null | 25.943249 | 708 | 0.531719 | true | 2,322 | Qwen/Qwen-72B | 1. YES
2. YES | 0.705785 | 0.853913 | 0.602679 | __label__eng_Latn | 0.991949 | 0.238555 |
### Imports
```python
#imports
from __future__ import print_function
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
import sys
import os
import pandas as pd
import time
import math
import random
```
### Initialisation Functions
```python
def calculate_minimu... | 0eda39a6e45423069740bc4ba2f034efd42cd66a | 55,539 | ipynb | Jupyter Notebook | Inventory Routing Problem/IVR_Final-Ammends.ipynb | Ellusionists/Thesis | 3c5b33ef2379b3ac5c8974de5c25b656bd410d26 | [
"Apache-2.0"
] | null | null | null | Inventory Routing Problem/IVR_Final-Ammends.ipynb | Ellusionists/Thesis | 3c5b33ef2379b3ac5c8974de5c25b656bd410d26 | [
"Apache-2.0"
] | null | null | null | Inventory Routing Problem/IVR_Final-Ammends.ipynb | Ellusionists/Thesis | 3c5b33ef2379b3ac5c8974de5c25b656bd410d26 | [
"Apache-2.0"
] | null | null | null | 39.305732 | 6,940 | 0.497218 | true | 16,298 | Qwen/Qwen-72B | 1. YES
2. YES | 0.874077 | 0.658418 | 0.575508 | __label__eng_Latn | 0.700192 | 0.175427 |
```python
# deep learning related tools
import sympy
import numpy as np
import tensorflow as tf
# quantum ML tools
import tensorflow_quantum as tfq
import cirq
import collections
# visualization tools (inline matploit only notebook needed)
%matplotlib inline
import matplotlib.pyplot as plt
from cirq.cont... | 1548d041694759a5ebac4399d89b8de7b6beef36 | 68,791 | ipynb | Jupyter Notebook | q_mnist.ipynb | HuangChiEn/tfq_tutorial_refactor | d64e1c62e8766fe28495763e161440b5219d4770 | [
"Apache-2.0"
] | null | null | null | q_mnist.ipynb | HuangChiEn/tfq_tutorial_refactor | d64e1c62e8766fe28495763e161440b5219d4770 | [
"Apache-2.0"
] | null | null | null | q_mnist.ipynb | HuangChiEn/tfq_tutorial_refactor | d64e1c62e8766fe28495763e161440b5219d4770 | [
"Apache-2.0"
] | null | null | null | 159.97907 | 23,610 | 0.616185 | true | 2,279 | Qwen/Qwen-72B | 1. YES
2. YES | 0.709019 | 0.743168 | 0.52692 | __label__eng_Latn | 0.648185 | 0.062542 |
# Numerical Methods in Scientific Computing
# Assignment 4
# Q1.
To compute $\int_0^1e^{x^2}dx$ using Trapezoidal rule and modified Trapezoidal rule.
- Trapezoidal Rule is given by,
\begin{equation}
\int_{x_0}^{x_N}f(x)dx = \frac{h}{2}\sum_{i=0}^{N-1} [f(x_i)+f(x_{i+1})] + O(h^2)
\end{equation}
- Trapezoidal Ru... | 290c012fc711f14eeb14283c14cbbb4583766300 | 113,333 | ipynb | Jupyter Notebook | me16b077_4.ipynb | ENaveen98/Numerical-methods-and-Scientific-computing | 5b931621e307386c8c20430db9cb8dae243d38ba | [
"MIT"
] | 1 | 2021-01-05T12:31:51.000Z | 2021-01-05T12:31:51.000Z | me16b077_4.ipynb | ENaveen98/Numerical-methods-and-Scientific-computing | 5b931621e307386c8c20430db9cb8dae243d38ba | [
"MIT"
] | null | null | null | me16b077_4.ipynb | ENaveen98/Numerical-methods-and-Scientific-computing | 5b931621e307386c8c20430db9cb8dae243d38ba | [
"MIT"
] | null | null | null | 211.048417 | 48,812 | 0.873064 | true | 7,115 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951142 | 0.882428 | 0.839314 | __label__eng_Latn | 0.158181 | 0.788342 |
```python
from sympy import *
from sympy.abc import *
import numpy as np
import matplotlib.pyplot as plt
init_printing()
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
```
```python
x = Function('x')
dxdt = Derivative(x(t), t)
dxxdtt = Derivative(x(t), t, t)
edo = Eq(dxxdtt - (k... | 1595f6f5a4e55a6bd5686b4716b3973c8d148032 | 19,075 | ipynb | Jupyter Notebook | Mec_Flu_I/Exercicios_recomendados_FoxMcDonalds.ipynb | Chabole/7-semestre-EngMec | 520e6ca0394d554e8de102e1b509ccbd0f0e1cbb | [
"MIT"
] | 1 | 2022-01-05T14:17:04.000Z | 2022-01-05T14:17:04.000Z | Mec_Flu_I/Exercicios_recomendados_FoxMcDonalds.ipynb | Chabole/7-semestre-EngMec | 520e6ca0394d554e8de102e1b509ccbd0f0e1cbb | [
"MIT"
] | null | null | null | Mec_Flu_I/Exercicios_recomendados_FoxMcDonalds.ipynb | Chabole/7-semestre-EngMec | 520e6ca0394d554e8de102e1b509ccbd0f0e1cbb | [
"MIT"
] | null | null | null | 87.100457 | 4,422 | 0.836697 | true | 221 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.774583 | 0.69534 | __label__eng_Latn | 0.229842 | 0.453839 |
# 低次元化
```python
import sympy as sy
from sympy.printing.numpy import NumPyPrinter
from sympy import julia_code
from sympy.utilities.codegen import codegen
import tqdm
import os
from pathlib import Path
from kinematics import Local
```
```python
# アクチュエータベクトル
l1, l2, l3 = sy.symbols("l1, l2, l3")
q = sy.Matrix([[l... | f7535eb999cc351164fff4d7bd1590032c0d0271 | 21,273 | ipynb | Jupyter Notebook | o/soft_robot/derivation_of_dynamics/reduction.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | o/soft_robot/derivation_of_dynamics/reduction.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | o/soft_robot/derivation_of_dynamics/reduction.ipynb | YoshimitsuMatsutaIe/ctrlab2021_soudan | 7841c981e6804cc92d34715a00e7c3efce41d1d0 | [
"MIT"
] | null | null | null | 131.314815 | 9,806 | 0.57077 | true | 6,311 | Qwen/Qwen-72B | 1. YES
2. YES | 0.912436 | 0.626124 | 0.571298 | __label__lmo_Latn | 0.031091 | 0.165647 |
Solution to: [Day 3: Drawing Marbles](https://www.hackerrank.com/challenges/s10-mcq-6/problem)
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
- Table of Contents
- Math Solution
- Facts
- Monte Carlo Solution
- Imports
- Constants
- Auxiliary functions
- Main
```javascript
%%ja... | c02358eb71fb8b59b600a4b84cc8a278fa0c9c54 | 5,699 | ipynb | Jupyter Notebook | statistics/10_days/11_day3drawingmarbles.ipynb | jaimiles23/hacker_rank | 0580eac82e5d0989afabb5c2e66faf09713f891b | [
"Apache-2.0"
] | null | null | null | statistics/10_days/11_day3drawingmarbles.ipynb | jaimiles23/hacker_rank | 0580eac82e5d0989afabb5c2e66faf09713f891b | [
"Apache-2.0"
] | null | null | null | statistics/10_days/11_day3drawingmarbles.ipynb | jaimiles23/hacker_rank | 0580eac82e5d0989afabb5c2e66faf09713f891b | [
"Apache-2.0"
] | 3 | 2021-09-22T11:06:58.000Z | 2022-01-25T09:29:24.000Z | 21.751908 | 130 | 0.519389 | true | 693 | Qwen/Qwen-72B | 1. YES
2. YES | 0.913677 | 0.880797 | 0.804764 | __label__eng_Latn | 0.82499 | 0.708068 |
# Logistic map explorations (Taylor and beyond)
Adapted by Dick Furnstahl from the Ipython Cookbook by Cyrille Rossant.
Lyapunov plot modified on 29-Jan-2019 after discussion with Michael Heinz.
Here we consider the *logistic map*, which illustrates how chaos can arise from a simple nonlinear equation. The logistic ... | 97d235cf99b041bdfc800d864b4ef97487847a24 | 126,205 | ipynb | Jupyter Notebook | 2020_week_3/Logistic_map_explorations.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_3/Logistic_map_explorations.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 2020_week_3/Logistic_map_explorations.ipynb | CLima86/Physics_5300_CDL | d9e8ee0861d408a85b4be3adfc97e98afb4a1149 | [
"MIT"
] | null | null | null | 296.952941 | 94,040 | 0.916176 | true | 3,297 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.835484 | 0.719801 | __label__eng_Latn | 0.991522 | 0.510671 |
# Monte Carlo Calculation of π
### Christina C. Lee
### Category: Numerics
### Monte Carlo Physics Series
* [Monte Carlo: Calculation of Pi](../Numerics_Prog/Monte-Carlo-Pi.ipynb)
* [Monte Carlo Markov Chain](../Numerics_Prog/Monte-Carlo-Markov-Chain.ipynb)
* [Monte Carlo Ferromagnet](../Prerequisites/Monte-Carlo-Fe... | 51563c8beb54d912180cc1a555b42cad79f62be6 | 627,343 | ipynb | Jupyter Notebook | Numerics_Prog/Monte-Carlo-Pi.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 22 | 2015-11-15T08:47:04.000Z | 2022-02-25T10:47:12.000Z | Numerics_Prog/Monte-Carlo-Pi.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 11 | 2016-02-23T12:18:26.000Z | 2019-09-14T07:14:26.000Z | Numerics_Prog/Monte-Carlo-Pi.ipynb | albi3ro/M4 | ccd27d4b8b24861e22fe806ebaecef70915081a8 | [
"MIT"
] | 6 | 2016-02-24T03:08:22.000Z | 2022-03-10T18:57:19.000Z | 125.318218 | 484 | 0.576669 | true | 1,883 | Qwen/Qwen-72B | 1. YES
2. YES | 0.826712 | 0.779993 | 0.644829 | __label__eng_Latn | 0.992451 | 0.336485 |
We have a square matrix $R$. We consider the error for $T = R'R$ where $R'$ is the transpose of $R$.
The elements of $R$ are $r_{i,j}$, where $i = 1 \dots N, j = 1 \dots N$.
$r_{i, *}$ is row $i$ of $R$.
Now let $R$ be a rotation matrix. $T$ at infinite precision will be the identity matrix $I$
Assume the maximum... | cf35cda2bee9298d5a963b9e546723741eeb8ee2 | 8,608 | ipynb | Jupyter Notebook | doc/source/notebooks/ata_error.ipynb | tobon/nibabel | ff2b5457207bb5fd6097b08f7f11123dc660fda7 | [
"BSD-3-Clause"
] | 1 | 2015-10-01T01:13:59.000Z | 2015-10-01T01:13:59.000Z | doc/source/notebooks/ata_error.ipynb | tobon/nibabel | ff2b5457207bb5fd6097b08f7f11123dc660fda7 | [
"BSD-3-Clause"
] | 2 | 2015-11-13T03:05:24.000Z | 2016-08-06T19:18:54.000Z | doc/source/notebooks/ata_error.ipynb | tobon/nibabel | ff2b5457207bb5fd6097b08f7f11123dc660fda7 | [
"BSD-3-Clause"
] | 1 | 2019-02-27T20:48:03.000Z | 2019-02-27T20:48:03.000Z | 34.294821 | 754 | 0.492565 | true | 2,292 | Qwen/Qwen-72B | 1. YES
2. YES | 0.928409 | 0.903294 | 0.838626 | __label__eng_Latn | 0.913562 | 0.786743 |
```python
import sympy as sp
sp.init_printing()
```
Analyitc solution for CSTR of 2 A -> B
```python
symbs = t, f, fcA, fcB, IA, IB, k, c1, c2 = sp.symbols('t f phi_A phi_B I_A I_B k c1 c2', real=True, positive=True)
symbs
```
```python
def analytic(t, f, fcA, fcB, IA, IB, k, c1, c2):
u = sp.sqrt(f*(f + 4*fcA*... | 68efe3a9973f0570150c175ba3cef510f8ef23a9 | 10,081 | ipynb | Jupyter Notebook | chempy/kinetics/tests/_derive_analytic_cstr_bireac.ipynb | Narsil/chempy | ac7217f45a8cfe3b11ca771f78f0a04c07708818 | [
"BSD-2-Clause"
] | null | null | null | chempy/kinetics/tests/_derive_analytic_cstr_bireac.ipynb | Narsil/chempy | ac7217f45a8cfe3b11ca771f78f0a04c07708818 | [
"BSD-2-Clause"
] | null | null | null | chempy/kinetics/tests/_derive_analytic_cstr_bireac.ipynb | Narsil/chempy | ac7217f45a8cfe3b11ca771f78f0a04c07708818 | [
"BSD-2-Clause"
] | 1 | 2022-03-21T09:01:48.000Z | 2022-03-21T09:01:48.000Z | 23.945368 | 155 | 0.472275 | true | 2,039 | Qwen/Qwen-72B | 1. YES
2. YES | 0.853913 | 0.851953 | 0.727493 | __label__kor_Hang | 0.158695 | 0.528543 |
# Solar Panel Power
This notebook calculates the various parameters of the shadow cast by dipole antennae on a panel under them using a simple Monte Carlo algorithm. The light source is assumed to be point-like and at infinity.
The setup is as follows:
We assume the antennae to be cylinders of radius $r_0=3cm$, at ... | f275813298c5ee0acd9d26b3d1506222ae910c79 | 142,173 | ipynb | Jupyter Notebook | solar_power/shadows.ipynb | lusee-night/notebooks | 688dec8d07f33d67f7891486b48a233525338eb7 | [
"MIT"
] | null | null | null | solar_power/shadows.ipynb | lusee-night/notebooks | 688dec8d07f33d67f7891486b48a233525338eb7 | [
"MIT"
] | null | null | null | solar_power/shadows.ipynb | lusee-night/notebooks | 688dec8d07f33d67f7891486b48a233525338eb7 | [
"MIT"
] | 2 | 2022-03-11T02:17:24.000Z | 2022-03-14T06:01:29.000Z | 271.8413 | 122,756 | 0.914105 | true | 3,691 | Qwen/Qwen-72B | 1. YES
2. YES | 0.893309 | 0.841826 | 0.752011 | __label__eng_Latn | 0.811205 | 0.585505 |
# Chasing the power of optimal length increase
(c) 2021 Tom Röschinger. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).
***
```julia
using ... | 8fd90ded242cab8b9747ad1ecb066b053b148064 | 779,053 | ipynb | Jupyter Notebook | notebooks/old_notebooks/3_l_opt_scaling.ipynb | tomroesch/complexity_evolution | 428905cf179b9fcfd2be3c1b5bb24e324241eb63 | [
"MIT"
] | null | null | null | notebooks/old_notebooks/3_l_opt_scaling.ipynb | tomroesch/complexity_evolution | 428905cf179b9fcfd2be3c1b5bb24e324241eb63 | [
"MIT"
] | null | null | null | notebooks/old_notebooks/3_l_opt_scaling.ipynb | tomroesch/complexity_evolution | 428905cf179b9fcfd2be3c1b5bb24e324241eb63 | [
"MIT"
] | null | null | null | 99.103549 | 8,840 | 0.627907 | true | 18,722 | Qwen/Qwen-72B | 1. YES
2. YES | 0.798187 | 0.672332 | 0.536646 | __label__eng_Latn | 0.529027 | 0.085139 |
# 1-D Convection-Diffusion equation
In this tutorial, we consider the **1D** convection-diffusion equation
$$
\frac{\partial u}{\partial t} + c \partial_x u - \nu \frac{\partial^2 u}{\partial x^2} = 0
$$
```python
# needed imports
from numpy import zeros, ones, linspace, zeros_like
from matplotlib.pyplot import plo... | bb0f5620cefda6a003b7ddc4cfc2fe5d4e2a9d56 | 37,596 | ipynb | Jupyter Notebook | lessons/Chapter2/03_convection_diffusion_1d.ipynb | ratnania/IGA-Python | a9d7aa9bd14d4b3f1b12cdfbc2f9bf3c0a68fff4 | [
"MIT"
] | 6 | 2018-04-27T15:40:17.000Z | 2020-08-13T08:45:35.000Z | lessons/Chapter2/03_convection_diffusion_1d.ipynb | GabrielJie/IGA-Python | a9d7aa9bd14d4b3f1b12cdfbc2f9bf3c0a68fff4 | [
"MIT"
] | 4 | 2021-06-08T22:59:19.000Z | 2022-01-17T20:36:56.000Z | lessons/Chapter2/03_convection_diffusion_1d.ipynb | GabrielJie/IGA-Python | a9d7aa9bd14d4b3f1b12cdfbc2f9bf3c0a68fff4 | [
"MIT"
] | 4 | 2018-10-06T01:30:20.000Z | 2021-12-31T02:42:05.000Z | 116.757764 | 10,652 | 0.872992 | true | 1,156 | Qwen/Qwen-72B | 1. YES
2. YES | 0.92944 | 0.828939 | 0.770449 | __label__eng_Latn | 0.642376 | 0.628344 |
```python
from resources.workspace import *
from IPython.display import display
from scipy.integrate import odeint
import copy
%matplotlib inline
```
# Lyapunov exponents and eigenvalues
A **Lypunov exponent** can be understood loosely as a kind of generalized eigenvalue for time-depenent linear transformations, or ... | 4bd4dedef8b077e30ad9a4a07aaeed49497564bc | 19,820 | ipynb | Jupyter Notebook | tutorials/DA and the Dynamics of Ensemble Based Forecasting/T3 - Lyapunov exponents and eigenvalues.ipynb | brajard/DAPPER | 1a513b2f23041b15fb335aeb17906607bf2a5350 | [
"MIT"
] | 3 | 2021-07-31T10:13:11.000Z | 2022-01-14T16:52:04.000Z | tutorials/DA and the Dynamics of Ensemble Based Forecasting/T3 - Lyapunov exponents and eigenvalues.ipynb | franktoffel/dapper | 373a27273ea109f349e5edcdcef0cfe0b83b925e | [
"MIT"
] | null | null | null | tutorials/DA and the Dynamics of Ensemble Based Forecasting/T3 - Lyapunov exponents and eigenvalues.ipynb | franktoffel/dapper | 373a27273ea109f349e5edcdcef0cfe0b83b925e | [
"MIT"
] | 3 | 2020-01-25T16:35:00.000Z | 2021-04-08T03:20:48.000Z | 39.325397 | 437 | 0.590061 | true | 4,349 | Qwen/Qwen-72B | 1. YES
2. YES | 0.782662 | 0.824462 | 0.645275 | __label__eng_Latn | 0.937955 | 0.337522 |
# Introduction to sympy
A Python library for symbolic computations
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Python-set-up" data-toc-modified-id="Python-set-up-1"><span class="toc-item-num">1 </span>Python set-up</a></span></li><li><sp... | 6c10dd658eb51a794c7dc029ec3cf318805b61c5 | 704,626 | ipynb | Jupyter Notebook | notebooks/Introduction to sympy.ipynb | bpalmer4/code-snippets | e87f7baade69ff25e062f54d58b3f6f611c4c283 | [
"MIT"
] | null | null | null | notebooks/Introduction to sympy.ipynb | bpalmer4/code-snippets | e87f7baade69ff25e062f54d58b3f6f611c4c283 | [
"MIT"
] | null | null | null | notebooks/Introduction to sympy.ipynb | bpalmer4/code-snippets | e87f7baade69ff25e062f54d58b3f6f611c4c283 | [
"MIT"
] | null | null | null | 113.81457 | 101,124 | 0.865276 | true | 12,022 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.887205 | 0.80261 | __label__eng_Latn | 0.713386 | 0.703065 |
# Lecture 4 - SciPy
What we have seen so far
- How to setup a python environment and jupyter notebooks
- Basic python language features
- Introduction to NumPy
- Plotting using matplotlib
Scipy is a collection of packages that provide useful mathematical functions commonly used for scientific computing.
List of sub... | f82878b6e9f70c96a8b6382506586465f50e3f08 | 140,109 | ipynb | Jupyter Notebook | nb/2019_winter/Lecture_4.ipynb | samuelcheang0419/cme193 | 609e4655544292a28dbb9ca0301637b006970af2 | [
"MIT"
] | null | null | null | nb/2019_winter/Lecture_4.ipynb | samuelcheang0419/cme193 | 609e4655544292a28dbb9ca0301637b006970af2 | [
"MIT"
] | null | null | null | nb/2019_winter/Lecture_4.ipynb | samuelcheang0419/cme193 | 609e4655544292a28dbb9ca0301637b006970af2 | [
"MIT"
] | null | null | null | 119.445013 | 23,060 | 0.888644 | true | 2,511 | Qwen/Qwen-72B | 1. YES
2. YES | 0.932453 | 0.83762 | 0.781042 | __label__eng_Latn | 0.724922 | 0.652954 |
<a href="https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/deeplearning.ai/nlp/c2_w4_model_architecture_relu_sigmoid.ipynb" target="_parent"></a>
# Word Embeddings: Intro to CBOW model, activation functions and working with Numpy
In this lecture notebook you will be given an introduct... | b7a3fe70edf7e4a63a0c4a98f2b2b6d4cc32fc11 | 24,317 | ipynb | Jupyter Notebook | deeplearning.ai/nlp/c2_w4_model_architecture_relu_sigmoid.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 8 | 2020-01-18T18:39:49.000Z | 2022-02-17T19:32:26.000Z | deeplearning.ai/nlp/c2_w4_model_architecture_relu_sigmoid.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | null | null | null | deeplearning.ai/nlp/c2_w4_model_architecture_relu_sigmoid.ipynb | martin-fabbri/colab-notebooks | 03658a7772fbe71612e584bbc767009f78246b6b | [
"Apache-2.0"
] | 6 | 2020-01-18T18:40:02.000Z | 2020-09-27T09:26:38.000Z | 25.650844 | 290 | 0.436361 | true | 2,186 | Qwen/Qwen-72B | 1. YES
2. YES | 0.845942 | 0.812867 | 0.687639 | __label__eng_Latn | 0.986346 | 0.435947 |
# Tutorial rápido de Python para Matemáticos
© Ricardo Miranda Martins, 2022 - http://www.ime.unicamp.br/~rmiranda/
## Índice
1. [Introdução](1-intro.html)
2. [Python é uma boa calculadora!](2-calculadora.html) [(código fonte)](2-calculadora.ipynb)
3. [Resolvendo equações](3-resolvendo-eqs.html) [(código font... | 63ffdb699bf6081f001ccf137f991eb90f4f756c | 32,190 | ipynb | Jupyter Notebook | 7-equacoes-diferenciais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 7-equacoes-diferenciais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 7-equacoes-diferenciais.ipynb | rmiranda99/tutorial-math-python | 6fe211f9cd0b8b93d4a0543a690ca124fee6a8b2 | [
"CC-BY-4.0"
] | null | null | null | 35.687361 | 530 | 0.567443 | true | 7,270 | Qwen/Qwen-72B | 1. YES
2. YES | 0.863392 | 0.890294 | 0.768673 | __label__por_Latn | 0.988519 | 0.624216 |
# An Introduction to Bayesian Statistical Analysis
Before we jump in to model-building and using MCMC to do wonderful things, it is useful to understand a few of the theoretical underpinnings of the Bayesian statistical paradigm. A little theory (and I do mean a *little*) goes a long way towards being able to apply th... | 11612d57cb023c1cad234e817257943816dba2a6 | 39,388 | ipynb | Jupyter Notebook | notebooks/Section1_1-Basic_Bayes.ipynb | AllenDowney/Bayes_Computing_Course | 9008ac3d0c25fb84a78bbc385eb73a680080c49c | [
"MIT"
] | 3 | 2020-08-24T16:26:02.000Z | 2020-10-16T21:43:45.000Z | notebooks/Section1_1-Basic_Bayes.ipynb | volpatto/Bayes_Computing_Course | f58f7655b366979ead4f15d73096025ee4e4ef70 | [
"MIT"
] | null | null | null | notebooks/Section1_1-Basic_Bayes.ipynb | volpatto/Bayes_Computing_Course | f58f7655b366979ead4f15d73096025ee4e4ef70 | [
"MIT"
] | 2 | 2020-10-11T08:53:45.000Z | 2022-01-03T08:49:00.000Z | 34.250435 | 542 | 0.617244 | true | 5,948 | Qwen/Qwen-72B | 1. YES
2. YES | 0.800692 | 0.880797 | 0.705247 | __label__eng_Latn | 0.997339 | 0.476857 |
<b>Construir o gráfico e encontrar o foco e uma equação da diretriz.</b>
<b>3. $y^2 = -8x$</b>
$2p = -8$,<b>logo</b><br><br>
$p = -4$<br><br><br>
<b>Calculando o foco</b><br><br>
$F = \frac{p}{2}$<br><br>
$F = \frac{-4}{2}$<br><br>
$F = -2$<br><br>
$F(-2,0)$<br><br><br>
<b>Calculando a diretriz</b><br><br>
$d = -\fra... | adada994be2375c2b4933c04f9d39674f8df1366 | 14,302 | ipynb | Jupyter Notebook | Problemas Propostos. Pag. 172 - 175/03.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | 1 | 2020-02-03T16:40:45.000Z | 2020-02-03T16:40:45.000Z | Problemas Propostos. Pag. 172 - 175/03.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | Problemas Propostos. Pag. 172 - 175/03.ipynb | mateuschaves/GEOMETRIA-ANALITICA | bc47ece7ebab154e2894226c6d939b7e7f332878 | [
"MIT"
] | null | null | null | 158.911111 | 12,456 | 0.896378 | true | 303 | Qwen/Qwen-72B | 1. YES
2. YES | 0.908618 | 0.774583 | 0.7038 | __label__por_Latn | 0.495126 | 0.473495 |
## Markov Networks
author: Jacob Schreiber <br>
contact: jmschreiber91@gmail.com
Markov networks are probabilistic models that are usually represented as an undirected graph, where the nodes represent variables and the edges represent associations. Markov networks are similar to Bayesian networks with the primary dif... | b0b52b79e94cdcd687b0c6100398f66e501fcdca | 19,162 | ipynb | Jupyter Notebook | tutorials/B_Model_Tutorial_7_Markov_Networks.ipynb | manishgit138/pomegranate | 3457dcefdd623483b8efec7e9d87fd1bf4c115b0 | [
"MIT"
] | 3,019 | 2015-01-04T23:19:03.000Z | 2022-03-31T12:55:46.000Z | tutorials/B_Model_Tutorial_7_Markov_Networks.ipynb | manishgit138/pomegranate | 3457dcefdd623483b8efec7e9d87fd1bf4c115b0 | [
"MIT"
] | 818 | 2015-01-05T10:15:57.000Z | 2022-03-07T19:30:28.000Z | tutorials/B_Model_Tutorial_7_Markov_Networks.ipynb | manishgit138/pomegranate | 3457dcefdd623483b8efec7e9d87fd1bf4c115b0 | [
"MIT"
] | 639 | 2015-01-05T04:16:42.000Z | 2022-03-29T11:08:00.000Z | 35.031079 | 843 | 0.593362 | true | 2,849 | Qwen/Qwen-72B | 1. YES
2. YES | 0.740174 | 0.805632 | 0.596308 | __label__eng_Latn | 0.995947 | 0.223754 |
```python
import holoviews as hv
hv.extension('bokeh')
hv.opts.defaults(hv.opts.Curve(width=500),
hv.opts.Scatter(width=500, size=4),
hv.opts.Histogram(width=500),
hv.opts.Slope(color='k', alpha=0.5, line_dash='dashed'),
hv.opts.HLine(color='k', alpha=... | 58b56443705523301d7026a5694c562b7ce6c275 | 22,109 | ipynb | Jupyter Notebook | lectures/5_linear_regression/part2.ipynb | magister-informatica-uach/INFO337 | 45d7faabbd4ed5b25a575ee065551b87b097f92e | [
"Unlicense"
] | 4 | 2021-06-12T04:07:26.000Z | 2022-03-27T23:22:59.000Z | lectures/5_linear_regression/part2.ipynb | magister-informatica-uach/INFO337 | 45d7faabbd4ed5b25a575ee065551b87b097f92e | [
"Unlicense"
] | null | null | null | lectures/5_linear_regression/part2.ipynb | magister-informatica-uach/INFO337 | 45d7faabbd4ed5b25a575ee065551b87b097f92e | [
"Unlicense"
] | 1 | 2019-11-07T14:49:09.000Z | 2019-11-07T14:49:09.000Z | 32.802671 | 429 | 0.531639 | true | 4,600 | Qwen/Qwen-72B | 1. YES
2. YES | 0.712232 | 0.73412 | 0.522864 | __label__eng_Latn | 0.935712 | 0.053117 |
# LASSO and Ridge Regression
This function shows how to use TensorFlow to solve lasso or ridge regression for $\boldsymbol{y} = \boldsymbol{Ax} + \boldsymbol{b}$
We will use the iris data, specifically: $\boldsymbol{y}$ = Sepal Length, $\boldsymbol{x}$ = Petal Width
```python
# import required libraries
import matp... | 8c6a18d5d7ff10a771fb835ee989ea944035f14a | 53,264 | ipynb | Jupyter Notebook | 03_Linear_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.ipynb | haru-256/tensorflow_cookbook | 18923111eaccb57b47d07160ae5c202c945da750 | [
"MIT"
] | 1 | 2021-02-27T16:16:02.000Z | 2021-02-27T16:16:02.000Z | 03_Linear_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.ipynb | haru-256/tensorflow_cookbook | 18923111eaccb57b47d07160ae5c202c945da750 | [
"MIT"
] | 2 | 2018-03-07T14:31:22.000Z | 2018-03-07T15:04:17.000Z | 03_Linear_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.ipynb | haru-256/tensorflow_cookbook | 18923111eaccb57b47d07160ae5c202c945da750 | [
"MIT"
] | null | null | null | 138.348052 | 24,392 | 0.885307 | true | 1,342 | Qwen/Qwen-72B | 1. YES
2. YES | 0.897695 | 0.752013 | 0.675078 | __label__eng_Latn | 0.310633 | 0.406764 |
<div style='background-image: url("../../share/images/header.svg") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>
<div style="float: right ; margin: 50px ; padding: 20px ; background: rgba(255 , 255 , 255 , 0.7) ; width: 50% ; height: 150px">
<div style="position: relative ; ... | e665286258e008e45064bf1ca26110b0a82086b1 | 8,825 | ipynb | Jupyter Notebook | notebooks/Computational Seismology/The Pseudospectral Method/ps_cheby_elastic_1d.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-07-11T10:01:39.000Z | 2020-12-16T14:26:03.000Z | notebooks/Computational Seismology/The Pseudospectral Method/ps_cheby_elastic_1d.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | null | null | null | notebooks/Computational Seismology/The Pseudospectral Method/ps_cheby_elastic_1d.ipynb | krischer/seismo_live_build | e4e8e59d9bf1b020e13ac91c0707eb907b05b34f | [
"CC-BY-3.0"
] | 3 | 2020-11-11T05:05:41.000Z | 2022-03-12T09:36:24.000Z | 8,825 | 8,825 | 0.541076 | true | 1,887 | Qwen/Qwen-72B | 1. YES
2. YES | 0.771844 | 0.651355 | 0.502744 | __label__eng_Latn | 0.693717 | 0.006372 |
## Linear Algebra
Linear algebra refers to the study of linear relationships. In this class, we will cover some basic concepts of linear algebra that are needed to understand some more advanced and *practical* concepts and definitions. If you are interested in the concepts related to linear algebra and application, th... | b70211afe93fde3e5139a2354ad681413b21e932 | 74,860 | ipynb | Jupyter Notebook | Linear_Algebra.ipynb | dguari1/BME3240_2021 | b069d6e6336f44dcb8d3ef79bbcf5410cde68dcc | [
"MIT"
] | 4 | 2021-08-28T03:42:39.000Z | 2021-11-04T17:14:29.000Z | Linear_Algebra.ipynb | dguari1/BME3240_2021 | b069d6e6336f44dcb8d3ef79bbcf5410cde68dcc | [
"MIT"
] | null | null | null | Linear_Algebra.ipynb | dguari1/BME3240_2021 | b069d6e6336f44dcb8d3ef79bbcf5410cde68dcc | [
"MIT"
] | null | null | null | 70.622642 | 18,056 | 0.768875 | true | 6,571 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.952574 | 0.899398 | __label__eng_Latn | 0.970079 | 0.927938 |
Problem: Heavy hitter
Reference:
- Privacy at Scale: Local Differential Privacy in Practice
```python
%load_ext autoreload
%autoreload 2
```
```python
%matplotlib inline
import matplotlib.pyplot as plt
```
Implementation of Random Response Protocol $\pi$ (for user with value $v$) as
\begin{equation}
\forall_{y\... | 8253beaf4767fb8cc934a0f47a1763fce1a025c5 | 14,932 | ipynb | Jupyter Notebook | 012119_random_response.ipynb | kinsumliu/notes | 3601c50a11966bed84c5d792778f3b103ba801d2 | [
"MIT"
] | null | null | null | 012119_random_response.ipynb | kinsumliu/notes | 3601c50a11966bed84c5d792778f3b103ba801d2 | [
"MIT"
] | null | null | null | 012119_random_response.ipynb | kinsumliu/notes | 3601c50a11966bed84c5d792778f3b103ba801d2 | [
"MIT"
] | null | null | null | 69.775701 | 8,562 | 0.783016 | true | 1,134 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.863392 | 0.781068 | __label__eng_Latn | 0.755536 | 0.653014 |
<!-- dom:TITLE: Week 42 Solving differential equations and Convolutional (CNN) -->
# Week 42 Solving differential equations and Convolutional (CNN)
<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, ... | d31d104f07f122737b3c909af4530f7cf9e17daa | 169,272 | ipynb | Jupyter Notebook | Lectures/week42.ipynb | adelezaini/MachineLearning | dc3f34f5d509bed6a993705373c46be4da3f97db | [
"MIT"
] | null | null | null | Lectures/week42.ipynb | adelezaini/MachineLearning | dc3f34f5d509bed6a993705373c46be4da3f97db | [
"MIT"
] | null | null | null | Lectures/week42.ipynb | adelezaini/MachineLearning | dc3f34f5d509bed6a993705373c46be4da3f97db | [
"MIT"
] | null | null | null | 37.466135 | 3,992 | 0.569982 | true | 32,633 | Qwen/Qwen-72B | 1. YES
2. YES | 0.699254 | 0.879147 | 0.614747 | __label__eng_Latn | 0.985281 | 0.266594 |
```python
import numpy as np
import matplotlib.pyplot as plt
import math
from mpl_toolkits.mplot3d import Axes3D
from scipy.ndimage.morphology import distance_transform_edt
```
#### Gradient Ascent
\begin{align}
\mathbf{r}_{i+1}&=\mathbf{r}_i+\eta\Delta \mathbf{r} \\
\Delta\mathbf{r} &\sim -\frac{\nabla \mathbf{f}}{... | 42294f67b92631841c11d70978aeb5a455ac1546 | 404,681 | ipynb | Jupyter Notebook | Robotics/PotentialFieldPlanPath/.ipynb_checkpoints/PotentialFieldPath-checkpoint.ipynb | zcemycl/ProbabilisticPerspectiveMachineLearning | 8291bc6cb935c5b5f9a88f7b436e6e42716c21ae | [
"MIT"
] | 4 | 2019-11-20T10:20:29.000Z | 2021-11-09T11:15:23.000Z | Computational Motion Planning/PotentialFieldPlanPath/PotentialFieldPath.ipynb | kasiv008/Robotics | 302b3336005acd81202ebbbb0c52a4b2692fa9c7 | [
"MIT"
] | null | null | null | Computational Motion Planning/PotentialFieldPlanPath/PotentialFieldPath.ipynb | kasiv008/Robotics | 302b3336005acd81202ebbbb0c52a4b2692fa9c7 | [
"MIT"
] | 2 | 2020-05-27T03:56:38.000Z | 2021-05-02T13:15:42.000Z | 2,013.338308 | 398,696 | 0.960658 | true | 1,058 | Qwen/Qwen-72B | 1. YES
2. YES | 0.923039 | 0.72487 | 0.669084 | __label__eng_Latn | 0.350791 | 0.392837 |
# Free-Body Diagram for particles
> Renato Naville Watanabe
> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab)
> Federal University of ABC, Brazil
<h1>Contents<span class="tocSkip"></span></h1><br>
<div class="toc"><ul class="toc-item"><li><span><a href="#Python-setup" data-toc-... | d46eb71f5674bf8e4cbe801e48106fa35c9fe2f3 | 235,130 | ipynb | Jupyter Notebook | notebooks/FBDParticles.ipynb | regifukuchi/BMC | 9983c94ba0aa8e3660f08ab06fb98e38d7b22f0a | [
"CC-BY-4.0"
] | 293 | 2015-01-17T12:36:30.000Z | 2022-02-13T13:13:12.000Z | notebooks/FBDParticles.ipynb | guinetn/BMC | ae2d187a5fb9da0a2711a1ed56b87a3e1da0961f | [
"CC-BY-4.0"
] | 11 | 2018-06-21T21:40:40.000Z | 2018-08-09T19:55:26.000Z | notebooks/FBDParticles.ipynb | guinetn/BMC | ae2d187a5fb9da0a2711a1ed56b87a3e1da0961f | [
"CC-BY-4.0"
] | 162 | 2015-01-16T22:54:31.000Z | 2022-02-14T21:14:43.000Z | 159.843644 | 34,004 | 0.856862 | true | 15,148 | Qwen/Qwen-72B | 1. YES
2. YES | 0.672332 | 0.826712 | 0.555825 | __label__eng_Latn | 0.654101 | 0.129696 |
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# Orthogonal Random Forest: Use Cases and Examples
Orthogonal Random Forest (ORF) combines orthogonalization,
a technique that effectively removes the confounding effect in two-stage estimati... | 32a8a1de360d6834234d2631e43ce9ab7fa6b27b | 157,964 | ipynb | Jupyter Notebook | notebooks/Orthogonal Random Forest Examples.ipynb | elissyah/econml | df89a5f5b7ba6089a38ed42e1b9af2a0bf2e0b1e | [
"MIT"
] | null | null | null | notebooks/Orthogonal Random Forest Examples.ipynb | elissyah/econml | df89a5f5b7ba6089a38ed42e1b9af2a0bf2e0b1e | [
"MIT"
] | null | null | null | notebooks/Orthogonal Random Forest Examples.ipynb | elissyah/econml | df89a5f5b7ba6089a38ed42e1b9af2a0bf2e0b1e | [
"MIT"
] | null | null | null | 126.776886 | 37,952 | 0.83438 | true | 9,879 | Qwen/Qwen-72B | 1. YES
2. YES | 0.763484 | 0.689306 | 0.526274 | __label__eng_Latn | 0.498999 | 0.061039 |
# Extracting Information from Audio Signals
## Measuring amplitude (Session 1.9) - Kadenze
### George Tzanetakis, University of Victoria
In this notebook we will explore different ways of measuring the amplitude of a sinusoidal signal. The use of the inner product to estimate the amplitude of a sinusoids in the ... | d5388e3e11af7625457d90f580ede4580157c601 | 144,311 | ipynb | Jupyter Notebook | course1/session1/kadenze_mir_c1_s1_9_measuring_amplitude.ipynb | Achilleasein/mir_program_kadenze | adc204f82dff565fe615e20681b84c94c2cff10d | [
"CC0-1.0"
] | 19 | 2021-03-16T00:00:29.000Z | 2022-02-01T05:03:45.000Z | course1/session1/kadenze_mir_c1_s1_9_measuring_amplitude.ipynb | femiogunbode/mir_program_kadenze | 7c3087acf1623b3b8d9742f1d50cd5dd53135020 | [
"CC0-1.0"
] | null | null | null | course1/session1/kadenze_mir_c1_s1_9_measuring_amplitude.ipynb | femiogunbode/mir_program_kadenze | 7c3087acf1623b3b8d9742f1d50cd5dd53135020 | [
"CC0-1.0"
] | 9 | 2021-03-16T03:07:45.000Z | 2022-02-12T04:29:03.000Z | 365.344304 | 39,396 | 0.93751 | true | 1,570 | Qwen/Qwen-72B | 1. YES
2. YES | 0.951142 | 0.890294 | 0.846796 | __label__eng_Latn | 0.966679 | 0.805725 |
[](https://pythonista.io)
# Introducción a ```sympy```.
El proyecto [sympy](https://www.sympy.org/en/index.html) comprende una biblioteca de herramientas que permiten realziar operaciones de matemáticas simbólicas.
En este sentido, es posible utilizar algunos de sus componentes para realizar operaciones que en lugar... | b269f0d194bfce9315d3009937ce9024cb20fa14 | 5,798 | ipynb | Jupyter Notebook | 15_introduccion_a_sympy.ipynb | PythonistaMX/py301 | 8831a0a0864d69b3ac6dc1a547c1e5066a124cde | [
"MIT"
] | 7 | 2019-05-14T18:23:29.000Z | 2021-12-24T13:34:16.000Z | 15_introduccion_a_sympy.ipynb | PythonistaMX/py301 | 8831a0a0864d69b3ac6dc1a547c1e5066a124cde | [
"MIT"
] | null | null | null | 15_introduccion_a_sympy.ipynb | PythonistaMX/py301 | 8831a0a0864d69b3ac6dc1a547c1e5066a124cde | [
"MIT"
] | 8 | 2018-12-25T23:09:33.000Z | 2021-09-13T04:49:52.000Z | 19.924399 | 406 | 0.481545 | true | 546 | Qwen/Qwen-72B | 1. YES
2. YES | 0.943348 | 0.868827 | 0.819606 | __label__spa_Latn | 0.495871 | 0.742551 |
```python
from __future__ import division, print_function
%matplotlib inline
```
```python
import sympy
from sympy import Matrix, eye, symbols, sin, cos, zeros
from sympy.physics.mechanics import *
from IPython.display import display
sympy.init_printing(use_latex='mathjax')
```
# Quaternion Math Functions
```pyth... | 6395ace740a1be84ac115a48b002d591d72ed911 | 26,564 | ipynb | Jupyter Notebook | modelDeriv/quadcopterMath.ipynb | lee-iv/sim-quadcopter | 69d17d09a70d11f724abceea952f17339054158f | [
"MIT"
] | 1 | 2020-07-30T00:16:29.000Z | 2020-07-30T00:16:29.000Z | modelDeriv/quadcopterMath.ipynb | lee-iv/sim-quadcopter | 69d17d09a70d11f724abceea952f17339054158f | [
"MIT"
] | null | null | null | modelDeriv/quadcopterMath.ipynb | lee-iv/sim-quadcopter | 69d17d09a70d11f724abceea952f17339054158f | [
"MIT"
] | null | null | null | 34.409326 | 1,153 | 0.322165 | true | 4,734 | Qwen/Qwen-72B | 1. YES
2. YES | 0.904651 | 0.766294 | 0.693228 | __label__yue_Hant | 0.223584 | 0.448932 |
Introduction
-------------------
Brzezniak (2000) is a great book because it approaches conditional expectation through a sequence of exercises, which is what we are trying to do here. The main difference is that Brzezniak takes a more abstract measure-theoretic approach to the same problems. Note that you *do* need t... | f4458f3c6719ed6059dec716f27c59f0f1991699 | 98,510 | ipynb | Jupyter Notebook | Conditional_expectation_MSE_Ex.ipynb | BadPhysicist/Python-for-Signal-Processing | a2565b75600359c244b694274bb03e4a1df934d6 | [
"CC-BY-3.0"
] | 10 | 2016-11-19T14:10:23.000Z | 2020-08-28T18:10:42.000Z | Conditional_expectation_MSE_Ex.ipynb | dougmcclymont/Python-for-Signal-Processing | a2565b75600359c244b694274bb03e4a1df934d6 | [
"CC-BY-3.0"
] | null | null | null | Conditional_expectation_MSE_Ex.ipynb | dougmcclymont/Python-for-Signal-Processing | a2565b75600359c244b694274bb03e4a1df934d6 | [
"CC-BY-3.0"
] | 5 | 2018-02-26T06:14:46.000Z | 2019-09-04T07:23:13.000Z | 135.130316 | 25,204 | 0.826617 | true | 4,982 | Qwen/Qwen-72B | 1. YES
2. YES | 0.861538 | 0.855851 | 0.737348 | __label__eng_Latn | 0.938744 | 0.551439 |
L2 error squared estimation
----------------------------
### Bilinear quad
```python
from sympy import *
from sympy.integrals.intpoly import polytope_integrate
from sympy.abc import x, y
```
```python
points = [ Point2D(-1, -1), Point2D(2, -2), Point2D(4, 1), Point2D(-2, 3)]
def phi_alpha_beta(alpha, beta, x, y)... | bea9d8022650f9cfba8e632e49ba455f80c860e9 | 4,541 | ipynb | Jupyter Notebook | notebooks/unit_tests_analytic_solutions.ipynb | InteractiveComputerGraphics/higher_order_embedded_fem | 868fbc25f93cae32aa3caaa41a60987d4192cf1b | [
"MIT"
] | 10 | 2021-10-19T17:11:52.000Z | 2021-12-26T10:20:53.000Z | notebooks/unit_tests_analytic_solutions.ipynb | InteractiveComputerGraphics/higher_order_embedded_fem | 868fbc25f93cae32aa3caaa41a60987d4192cf1b | [
"MIT"
] | null | null | null | notebooks/unit_tests_analytic_solutions.ipynb | InteractiveComputerGraphics/higher_order_embedded_fem | 868fbc25f93cae32aa3caaa41a60987d4192cf1b | [
"MIT"
] | 3 | 2021-10-20T16:13:05.000Z | 2022-03-16T01:50:35.000Z | 25.088398 | 222 | 0.473244 | true | 766 | Qwen/Qwen-72B | 1. YES
2. YES | 0.944177 | 0.815232 | 0.769724 | __label__eng_Latn | 0.581936 | 0.626658 |
# CS 224n Assignment #2: word2vec
## Understanding word2vec
Let’s have a quick refresher on the word2vec algorithm. The key insight behind word2vec is that ‘a word is known by the company it keeps’. Concretely, suppose we have a ‘center’ word c and a contextual window surrounding c. We shall refer to words that lie in... | f5e9a91cb04b45a9e47abd80303d5207f6e557b1 | 103,855 | ipynb | Jupyter Notebook | a2.ipynb | beunouah/cs224n | 9b23d573e72979108c09c68b9c687e265ff40e66 | [
"MIT"
] | null | null | null | a2.ipynb | beunouah/cs224n | 9b23d573e72979108c09c68b9c687e265ff40e66 | [
"MIT"
] | null | null | null | a2.ipynb | beunouah/cs224n | 9b23d573e72979108c09c68b9c687e265ff40e66 | [
"MIT"
] | null | null | null | 62.151406 | 27,546 | 0.592268 | true | 15,117 | Qwen/Qwen-72B | 1. YES
2. YES | 0.867036 | 0.847968 | 0.735218 | __label__eng_Latn | 0.70415 | 0.54649 |
# A Gentle Introduction to HARK: Buffer Stock Saving
This notebook explores the behavior of a consumer identical to the perfect foresight consumer described in [Gentle-Intro-To-HARK-PerfForesightCRRA](https://econ-ark.org/materials/Gentle-Intro-To-HARK-PerfForesightCRRA) except that now the model incorporates income ... | 27816ac87a447f411287f19460dbd3be22fa14cb | 78,193 | ipynb | Jupyter Notebook | notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb | frankovici/DemARK | 177c09bd387160d06f979c417671b3de18746846 | [
"Apache-2.0"
] | null | null | null | notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb | frankovici/DemARK | 177c09bd387160d06f979c417671b3de18746846 | [
"Apache-2.0"
] | null | null | null | notebooks/Gentle-Intro-To-HARK-Buffer-Stock-Model.ipynb | frankovici/DemARK | 177c09bd387160d06f979c417671b3de18746846 | [
"Apache-2.0"
] | null | null | null | 93.756595 | 11,488 | 0.811709 | true | 8,244 | Qwen/Qwen-72B | 1. YES
2. YES | 0.749087 | 0.709019 | 0.531117 | __label__eng_Latn | 0.971561 | 0.072293 |
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