What is a transformer in deep learning?

The Transformer is a deep learning model introduced in 2017 that utilizes the mechanism of attention, weighing the influence of different parts of the input data. For example, if the input data is a natural language sentence, the Transformer does not need to process the beginning of it before the end.

Is transformer better than Lstm?

To summarise, Transformers are better than all the other architectures because they totally avoid recursion, by processing sentences as a whole and by learning relationships between words thank’s to multi-head attention mechanisms and positional embeddings.

Is the transformer autoregressive?

Autoregressive Transformers And they succeeded: Transformers can actually be used for autoregression and hence for text generation.

How do you fix a vanishing gradient problem?

The simplest solution is to use other activation functions, such as ReLU, which doesn’t cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers.

What is vanishing gradient problem in CNN?

In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value.

Does ReLU have vanishing gradient?

I found rectified linear unit (ReLU) praised at several places as a solution to the vanishing gradient problem for neural networks. That is, one uses max(0,x) as activation function.

How do you stop gradient exploding vanishing?

How to Fix Exploding Gradients?

Re-Design the Network Model. In deep neural networks, exploding gradients may be addressed by redesigning the network to have fewer layers.
Use Long Short-Term Memory Networks.
Use Gradient Clipping.
Use Weight Regularization.

Why does ReLu help vanishing gradient?

It takes on negative values when z<0, which allows the unit to have an average output closer to 0(do not turn them to 0 as ReLU does). This helps solve the vanishing gradients problem. The hyperparameter α controls the value to which an ELU saturates when z is a large negative number.

What is vanishing exploding gradient problem?

In a network of n hidden layers, n derivatives will be multiplied together. If the derivatives are large then the gradient will increase exponentially as we propagate down the model until they eventually explode, and this is what we call the problem of exploding gradient .

What is vanishing gradient descent?

The term vanishing gradient refers to the fact that in a feedforward network (FFN) the backpropagated error signal typically decreases (or increases) exponentially as a function of the distance from the final layer. — Random Walk Initialization for Training Very Deep Feedforward Networks, 2014.