The below is the architecture of the attention mechanism.

Attention -Mechanism Architecture

Below is the individual components explained

The below is the architecture of the attention mechanism.

Attention -Mechanism Architecture

Below is the individual components explained

Below is the step by step explaination of the sequence to sequence model using convolution.

Each block of code is explained in detail

Step1.Tokenize English and German text from a string into a list of strings

def tokenize_de(text):

return [tok.text for tok in spacy_de.tokenizer(text)]

def tokenize_en(text):

return [tok.text for tok in spacy_en.tokenizer(text)]

Step2:

The below diagram explains encoder convolution:

Encoder Convolution:

- Login to Floyd hub and check your workspace

2. Launch the Python program for which you want to run the cprofiler.In this case its TestProfile.py which is nothing but Assignment14

These 5 functions can be found in:

https://jovian.ml/sudhakarmlal/01-tensor-operations

- torch.zeros : creates an tensor(out of matrix) with all values 0
- torch.view : re-sizes the tensor to specific dimension
- torch.add : adds two tensors
- torch.copy_ : copies one tensor to the other
- torch.to : to move the tensor to a specific device…

The Bayesian Theorem is derived from the following conditional probability

P(A ^ B) = P (A) P(B/A)

P(B^A) = P(B)P(A/B)

From the above

P(A) P(B/A) = P(B) P(A/B)

Which can further be simplified to

P(B/A) = P(A/B)P(B) / P(A)

Let's try to re-write the above equation for a set of…