In the previous stories we discussed about Transformers models and their application and did some detailed discussion about the Encoder blocks architecture. In this article we are going to look more on Decoder blocks, the another main building block of the transformers.

The architecture of the Decoder is similar to…


In the previous article we talked about the transformers models and their application in different use cases. In this article we are going to dive deep into the architecture of the Encoder blocks, one of the main building block of a transformer model.

The encoding component in transformer model is…


Transformers generally Language models which means they are trained on large collection of text data and they are capable of doing various NLP task such as:

  1. Text classification
  2. Text Summarization
  3. Sentiment Classification
  4. Text Generation and many more…

Transformers are also very large model which consists of huge number of parameters…


Machine Learning has become popular to the ability of systems to learn patterns from the data and predict. Availability of a large volume of data and improvements on the computing power of systems makes it more accessible and easy with few lines of code. To train/learn a system required a…


Dummy variable by definition is the numerical values that represent the categorical variable in the data and can take only 0 and 1. When it comes to machine learning problems we always feed numerical values to the algorithm to understand the data so we usually use numerical representation for categorical…


Generally we can say that Linear Discriminant Analysis is a dimensionality reduction technique like PCA( principle component analysis) but LDA is a supervised machine learning method while PCA is not. …


In this article I’m pointing some features of random forest that make it better model

  1. Random forest is an ensemble model which combine multiple models(decision trees) in parallel and do the final prediction. Which reduce the over-fitting issue.
  2. Random in random forest means it will fit multiple trees in the random samples of the data(bootstrap samples).This …

Vivek Muraleedharan

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