RNN vs CNN

What is The Main Difference between RNN and CNN | NLP

The main difference between RNN and CNN come from their structure of the Neural Network. Due to their specific design, CNNs are more fit for spatial data such as images whereas RNNs are more for temporal data that comes in sequence.

CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.

Continue reading

What is the main difference between RNN and LSTM | NLP | RNN vs LSTM

The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different in LSTM than RNN by which LSTMs are capable of maintaining long term temporal dependencies (remembering information for long period of time).

Language Models in NLP

Writing an email is something we do while walking on the road also. The most official mode of communication, So, have you ever seen the ‘Smart Compose’ feature automatically working in your Gmail giving you  instant ideas to finish sentences while composing an email? This is one of the different use-instances of language models utilized in Natural Language Processing (NLP). A language model is the core heart of the present day Natural Language Processing (NLP) domain. It’s a measurable device that investigates the example of human language for the forecast of words.

Beginners Guide to Text Classification | Machine Learning | NLP | part 8

In this post, we will develop a classification model where we’ll try to classify the movie reviews on positive and negative classes. I have used different machine learning algorithm to train the model and compared the accuracy of those models at the end. you can keep this post as a template to use various machine learning algorithms in python for text classification.

At the end we will validate the model by passing a random review to the trained model and understand the output class predicted by the model. You will learn how to create and use the pipeline for numerical feature extraction and model training together as a one function.

Numerical Feature Extraction from Text | NLP series | Part 6

Machine Learning algorithms don’t understand the textual data rather it understand only numerical data. So the problem is how to convert the textual data to the numerical features and further pass these numerical features to the machine learning algorithms.

As we all know that the raw text stored in some dump repository contains a lot of meaningful information. And in today’s fast changing world, it becomes essential to consider data driven decision than fully rely on experience driven decision.

1 2