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Machine Learning | Deep Learning | Artificial Intelligence

In this blog let’s try to understand how to fine-tune pre-trained model(BERT) on one of the Kaggle competitions which is 'Disaster Tweets Prediction’.
By the end of this article, you will be able to use BERT on your own dataset.

So let’s get started!

Google-BERT

Data Understanding:
In this dataset we have tweets and their corresponding labels like 0 and 1. if the tweet belongs to disaster then it is labeled as 1 otherwise 0. so after model training our model should be able to predict whether the tweet belongs to disaster or not.


Last week I completed a free course from hugging face, where I have learned about a lot of new things like transformers and many more pre-trained NLP models.

In this blog, I will share what pipeline object can do in the transformers library.

https://utilityanalytics.com/2020/07/natural-language-processing-talking-in-a-way-machines-can-understand/

What is Hugging Face?

Hugging Face Provides a lot of pre-trained models which are trained on billions of text corpus and a wide variety of NLP tasks. one pre-trained model that you may already know is BERT. There is also a lighter version of BERT which is DSITIL BERT.

All of these Pretrained models are based on…


1. Project idea, How it started?

Last month I completed my deep learning course as part of my PGD from IIIT Bangalore, I have done many projects based on CNN and RNN, LSTM but with the publicly available data. Even though those projects and assignments looks cool but that doesn’t gave me much satisfaction. so, I decided to do one project from scratch.

2. why this project?

I live in a village! so I choose an agriculture-based project and it also very easy for me to collect the data.To collect the data first I need to think of a problem…


Every ML beginner starts their journey in machine learning with the linear regression algorithm and it is the most easiest algorithm to understand.

In this article let’s try to understand Linear regression along with python implementation from scratch. I will try to cover both theory and practical implementation in python simultaneously.

Introduction:

when I first studied linear regression I have struggled a lot to understand this simple algorithm and I could not able to understand simple train test split and many more things and I don’t want this to happen for you so, if you are a beginner in ml this…


Photo by Nicolas Lobos on Unsplash

In my previous post, I talked about theory related to ridge and lasso regression and math equations behind them.

In this article let’s implement ridge and lasso regression in python.

note: If you don’t know the maths and theory concepts behind ridge and lasso I highly recommend you to follow this link: https://manojgadde.medium.com/ridge-and-lasso-regression-made-easy-343df45a90b9#4be1-463c284cfb2d

Introduction :

Fortunately implementing any machine learning algorithm is not much difficult and the Scikit-learn library provides many machine learning algorithms. let’s implement ridge and lasso regression using scikit-learn library.

In this, we are using the surprise housing dataset and we are required to build a regression model using…


Photo by Greg Rakozy on Unsplash

In this article let’s understand two different techniques in regularised regression

  1. Ridge Regression
  2. Lasso Regression

let’s start our learning by understanding what is Regularisation?

what is Regularisation?
we often face situations in ml where our model performs very well on training data and it fails to generalize on unseen data(overfitting condition) or (low bias and high variance condition). regularisation is a method to reduce the complexity of the model or it is a process to create an optimally complex model. …

Manoj Gadde

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