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Vaishnav Nagnath Kumbhar

7 posts •
Machine Learning (ML)

Extractive vs Abstractive Summarization

In this article at OpenGenus, we have explored the differences between Extractive and Abstractive Summarization in depth and presented the differences in a table.

Vaishnav Nagnath Kumbhar
Deep Learning

ChatGPT vs Google BARD

Two distinct language models, ChatGPT and Google BARD, were created by two separate businesses, OpenAI and Google, respectively. Even if they have certain things in common, they also differ greatly.

Vaishnav Nagnath Kumbhar
Machine Learning (ML)

Multi-output learning and Multi-output CNN models

In this article at OpenGenus, we have explored the part of Deep Learning where a model is trained to produce multi-outputs (more than 1) in contrast to standard Deep Learning models like ResNet50 for Image Recognition.

Vaishnav Nagnath Kumbhar
Machine Learning (ML)

Bias Variance tradeoff

An essential idea in statistical learning and machine learning is the bias-variance tradeoff. It speaks to the connection between a model's complexity and its precision in fitting the data.

Vaishnav Nagnath Kumbhar
Machine Learning (ML)

Region of Interest and ROI Pooling

In computer vision and image processing, region of interest (ROI) and ROI pooling are crucial ideas. In typical operations like object identification, segmentation, and tracking.

Vaishnav Nagnath Kumbhar
Machine Learning (ML)

AUC (Area Under The Curve) and ROC (Receiver Operating Characteristics)

In binary classification issues, the metrics AUC (Area Under the Curve) and ROC (Receiver Operating Characteristics) are frequently employed. AUC is a numerical metric that measures the performance of the classifier, whereas ROC is a graphical plot that shows the performance of a binary classifier.

Vaishnav Nagnath Kumbhar
Machine Learning (ML)

3 Types of Naive Bayes

In this article, we have explored the 3 different types of Naive Bayes classification algorithm in depth.

Vaishnav Nagnath Kumbhar
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