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Apoorva Agrawal in Data Science Group, IITR

Sep 29, 2017

Loss Functions and Optimization Algorithms. Demystified.

10 responses

Apoorva Agrawal in Data Science Group, IITR

Mar 31, 2017

Logistic Regression. Simplified.

After the basics of Regression, it’s time for basics of Classification. And, what can be easier than Logistic Regression!

This is what Classification actually means:

6 responses

Manjeet Singh in Data Science Group, IITR

Sep 4, 2017

Artistic Style Transfer with Convolutional Neural Network

We all have used apps like Prisma and Lucid, but ever wondered how these things works? Like we give a photo from our camera roll and select a design to mix both the images and we get a new image which has the content of our input…

5 responses

Sanchit Minocha in Data Science Group, IITR

Mar 30, 2017

Linear Regression. Back to Basics.

You have been waiting for this since Day 1, ain’t it? Because we like it unconventional, we saved it for the last. Next 2 days also cover Regression.

Linear Regression is the oldest, simple and widely used supervised machine learning algorithm…

HARSH SINGHAL in Data Science Group, IITR

Jun 17, 2017

Convolutional Neural Network with TensorFlow implementation

When you hear about deep learning breaking…

2 responses

Roopkishor Singh in Data Science Group, IITR

May 18, 2017

Singular Value Decomposition. Elucidated.

Mathematics is building block of Machine learning. I know math is hard to understand but it is much needed as well. Singular value decomposition (SVD) is one mathematical method used in various applications.

Akhil Gupta in Data Science Group, IITR

Mar 21, 2017

Decision Trees. Decoded.

It’s time to give the Algorithm series, an informative start. Here, we start with one of the most famous category i.e. Tree Based Models, which consists of Decision Trees, Random Forest and Boosting methods.

4 responses