Name: Saransh Chopra
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from manim import * | |
class Intro(Scene): | |
config.background_color = YELLOW_C | |
def construct(self): | |
g = NumberPlane() | |
self.add_sound( |
Name: Saransh Chopra
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def sigmoid(self, z): | |
""" | |
Returns sigmoid value. | |
""" | |
return 1 / (1 + np.exp(-z)) |
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import numpy as np | |
from sklearn.datasets import load_digits | |
class LogisticRegression: | |
""" | |
Logistic Regression using neural network. | |
Parameters | |
========== | |
X : np.ndarray |
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def fit(self): | |
""" | |
Maths involved - | |
z = w.T * x + b | |
y_predicted = a = sigmoid(z) | |
dw += (1 / m) * x * dz | |
db += dz | |
Gradient descent - | |
w = w - α * dw | |
b = b - α * db |
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def predict(self, x): | |
""" | |
Predicts the y values based on the training data. | |
""" | |
prediction = [] | |
for single_data in x: | |
prediction.append( | |
1 if self.sigmoid(np.dot(single_data, self.W) + self.b) > 0.5 else 0 | |
) |
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import googleapiclient.discovery | |
from creds import API_KEY | |
import youtube_dl | |
import os | |
# API information | |
api_service_name = "youtube" | |
api_version = "v3" | |
DEVELOPER_KEY = API_KEY |
- UnROOT.jl -- ROOT I/O in pure Julia: https://github.com/JuliaHEP/UnROOT.jl
- UnROOT.jl JOSS paper: https://joss.theoj.org/papers/bab42b0c60f9dc7ef3b8d6460bc7229c
- HiggsCombine / HistFactory in pure Julia: https://github.com/JuliaHEP/LiteHF.jl
- Histogram + plotting in pure Julia: https://moelf.github.io/FHist.jl/dev/notebooks/makie_plotting/
- Benchmark: https://github.com/Moelf/UnROOT_RDataFrame_MiniBenchmark
more links:
- JuliaHEP organization: https://github.com/JuliaHEP/
- Julia official discourse HEP tag: https://discourse.julialang.org/tag/hep, also join us at
#hep
on official slack. - mailist: https://groups.google.com/g/julia-hep