Дата регистрации 21 Апрель 2025
24 ПродажGradiyent tushish
![FOYDALANILGAN ADABIYOTLAR
1. Cybenko, G. 1989. Approximation by superpositions of a sigmoidal
function Mathematics of Control, Signals, and Systems, 2(4), 303–314.
2. Linnainmaa, Seppo (1970). The representation of the cumulative
rounding error of an algorithm as a Taylor expansion of the local rounding errors
(Masters) (in Finnish). University of Helsinki. pp. 6–7.
3. Kelley, Henry J. (1960). "Gradient theory of optimal flight paths".
ARS Journal. 30 (10): 947–954. doi:10.2514/8.5282.
4. Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and
the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
5. Werbos, Paul (1982). "Applications of advances in nonlinear
sensitivity analysis" (PDF). System modeling and optimization. Springer. pp. 762–
770. Archived (PDF) from the original on 14 April 2016. Retrieved 2 July 2017.
6. Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams.
"Learning Internal Representations by Error Propagation". David E. Rumelhart,
James L. McClelland, and the PDP research group. (editors), Parallel distributed
processing: Explorations in the microstructure of cognition, Volume 1:
Foundation. MIT Press, 1986.
7. Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements
of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New
York, NY, 2009.
8. Rosenblatt, Frank (1958). "The Perceptron: A Probabilistic Model For
Information Storage And Organization in the Brain". Psychological Review. 65
(6): 386–408. CiteSeerX 10.1.1.588.3775. doi:10.1037/h0042519. PMID
13602029. S2CID 12781225.
9. Rosenblatt, Frank (1962). Principles of Neurodynamics. Spartan, New
York.
10. Schmidhuber, Juergen (2022). "Annotated History of Modern AI and
Deep Learning". arXiv:2212.11279 [cs.NE].
19](https://docx.uz/documents/6ff4efcb-bacb-4220-8643-431dc3ea906e/page_18.png?v=1)
![ILOVA
Gradiyent tushishi (Gradient Descent) algoritmi Python dasturlash tilida
hamda sun'iy intellekt sohasida mashina o'qitish uchun ko'p foydalaniladigan
metodlardan biridir. Quyidagi kodlar, o'zgaruvchilarni optimallashtirish maqsadida
gradiyent tushishi algoritmini bajaruvchi oddiy bir Python skriptini ko'rsatadi:
import numpy as np
def gradient_descent(X, y, learning_rate, iterations):
"""
Gradiyent tushishi algoritmi.
X: Ma'lumotlar
y: Maqsad qiymatlar
learning_rate: O'rganish tezligi
iterations: Yangilanishlar soni
"""
m, n = X.shape # m - obyektlar soni, n - o'zgaruvchilar soni
theta = np.zeros((n, 1)) # O'zgaruvchilarning boshlang'ich qiymatlari
for _ in range(iterations):
# Gradiyent tushish formulasi
gradient = (1/m) * X.T.dot(X.dot(theta) - y)
# O'zgaruvchilarni yangilash
theta = theta - learning_rate * gradient
return theta
# Boshqa funksiyonlar va parametrlar
# ...
# Ma'lumotlar va maqsad qiymatlar
X = np.random.rand(100, 2) # Obyektlar va o'zgaruvchilar
y = 3 * X[:, 0].reshape(-1, 1) + 4 * X[:, 1].reshape(-1, 1) + np.random.randn(100,
1) * 0.5 # Tasodifiy maqsad qiymatlar
# Gradiyent tushishni bajarish
20](https://docx.uz/documents/6ff4efcb-bacb-4220-8643-431dc3ea906e/page_19.png?v=1)
GRADIYENT TUSHISH.docx