### Important Deep learning Concept Explained Part – 2

- Naveen
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**Converge**

Algorithm that converges will eventually reach an optimal answer, even if very slowly. An algorithm that doesn’t converge may never reach an optimal answer.

**Learning Rate**

Rate at which optimizers change weights and biases. High learning rate generally trains faster but risks not converging whereas a lower rate trains slower.

**Numerical instability**

Issues with very large/small values due to limits of floating-point numbers in computers.

**Embeddings**

Mapping from discrete objects. Such as words, to vectors of real numbers. Useful because classifier/neural networks work well on vectors of real numbers.

**Convolutional layer**

Series of convolutional operations, each acting a different slice of the input matrix.

**Dropout**

Method for regularization that involves ending training early.

**Gradient descent**

Technique to minimize loss by computing the gradients of loss with respect to the model’s parameters, conditioned on training data.