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Backpropagation Algorithm in Neural Networks

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14 Questions

What do (z_1)² and (z_2)² represent in the context of the neural network?

The sums of the multiplication between every input x_i with the corresponding weight (W_ij)¹ for layer 2

Which part of the neural network produces the predicted value?

Output layer

What is the purpose of forward propagation in a neural network?

To evaluate the predicted output s against an expected output y

Which popular NLP model is not specifically mentioned in the text?


What use case of transformers is NOT mentioned in the text?

Speech recognition

In what context did Denis Rothman deliver AI solutions for Moët et Chandon and Airbus?

Natural Language Processing (NLP)

Что является первой главной компонентой в методе главных компонент (PCA)?

Производная переменная, образованная в качестве линейной комбинации исходных переменных, объясняющая наибольшую дисперсию

Как можно определить i-ю главную компоненту в методе главных компонент (PCA)?

Это направление, ортогональное первым i − 1 главным компонентам, максимизирующее дисперсию проекции данных

Чем PCA тесно связано с анализом факторов?

Факторный анализ инкорпорирует больше предположений о структуре данных и решает собственные векторы немного другой матрицы

The relative error is a more appropriate metric than the absolute difference when comparing numerical and analytic gradients.


It is recommended to track the difference between the numerical and analytic gradients directly to determine their compatibility.


Using double precision floating-point arithmetic can reduce relative errors in gradient checking.


The deeper the neural network, the lower the relative errors are expected to be during gradient checking.


Normalizing the loss function over the batch can reduce relative errors in gradient computations.


Learn about the backpropagation algorithm, a fundamental building block in neural networks, first introduced in the 1960s and popularized in 1989 by Rumelhart, Hinton, and Williams. Discover how it effectively trains neural networks through the chain rule method, adjusting the model after each forward pass through the network.

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