목록Neural Networks and Deep Learning (4)

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[Neural Networks and Deep Learning] 4주차

Deep neural network notation Forward propagation in a deep network Parameters W[l] and b[l] Vectorized implementation Intuition about deep representation Forward and Backward fuctions Forward propagation for layer l Backward propagation for layer l What are hyperparameters?
부트캠프/Google ML Bootcamp 2023. 9. 5. 15:22
[Neural Networks and Deep Learning] 3주차

Neural Network Representation Computing a Neural Network's Output Vectorizing across multiple examples Justification for vectorized implementation Activation functions Why do you need Non-Linear Activation Functions? Derivatives of Activation Functions Gradient descent for neural networks Formulas for computing derivatives What happens if you initialize weights to zero? Random initialization
부트캠프/Google ML Bootcamp 2023. 9. 4. 13:28
[Neural Networks and Deep Learning] 2주차

Linear Regression Logistic Regression Logistic Regression cost function Gradient Descent Logistic Regression Gradient Descent Logistic Regression Gradient Descent on m examples Vectorization import numpy as np import time a = np.random.rand(1000000) b = np.random.rand(1000000) tic = time.time() c = 0 for i in range(1000000) : c += a[i] * b[i] toc = time.time() print('for loop :' + str(1000 * (to..
부트캠프/Google ML Bootcamp 2023. 9. 1. 19:20
[Neural Networks and Deep Learning] 1주차

Supervised Learning with Neural Networks Neural Network examples Scaled drives deep learning progress
부트캠프/Google ML Bootcamp 2023. 8. 30. 18:41
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