Blogs

Resnet Strikes back

Resnet Strikes Back

Understand how the model, training architecture and randomness are stitched together in deep learning. This blog contains notes from the amazing paper Resnet strikes back by Wightman et al.

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Vishnu Subramanian
Kaggle chaii comp

Exploring Petfinder.my - Pawpularity Contest Kaggle competition

This competition aims at finding out popularity of the shelter pets to speed up their adoption process. This post explains problem statement, plays with evaluation matric rmse and provide some key techniques that can be used for strong finish on kaggle leaderboard. We have even Provided sample start code to give you a headstart.

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Nischay Dhankhar
Kaggle chaii comp

How to begin with Chaii — Hindi, and Tamil Question Answering competition

Explore how to start with the Kaggle competition chaii - Hindi and Tamil Question Answering. Blog explains problem statement, plays with evaluation matric and provide ssome key techniques that can be used for strong finish on kaggle leaderboard..

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Nischay Dhankhar
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How to train models on multiple GPUs using fastai

Quick start guide to train deep learning models in multi-GPU setup using fastai

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Vishnu Subramanian
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Understanding and Building Resnets from scratch using Pytorch

Resnets are the go-to architecture for most of the deep learning tasks. What makes them so popular? Resnets introduced residual blocks which made deeper neural networks possible. In this post, Poonam Ligade shares understanding of resnets and shows how you can build Resnet from scratch using Pytorch.

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Poonam Ligade
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Identifying people without Masks - Using Deep Learning/AI

Vishnu Subramanian takes you through high level steps required for building an object detection pipeline for identifying persons without masks. He also discusses the challenges faced with this approach. Along with that he shares how we can use deep learning algorithms to help us in semi-automating the labelling task.

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Vishnu Subramanian
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Building image segmentation algorithm using fastai2/PyTorch
Part - 1

The first post in series introduces the problem of identifying salt deposits beneath the earth, describes why we choose fastai2 and build a data pipeline required for training a segmentation algorithm.

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Vishnu Subramanian
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Building image segmentation algorithm using fastai2/PyTorch
Part - 2

Learn about how to build a unet model for image segmentation with a custom encoder. Along the way you will build a solution which ranks in the top 4% of kaggle leaderboards. We will also create a pipeline required for predicting the test data, applying custom TTA and generating a kaggle submission file.

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Vishnu Subramanian
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Understanding Autoencoders and Variational Autoencoders

In the recent years, computer vision algorithms have achieved many things. One amazing and dangerous thing they can do is generate new images, faces, voices, etc. Let's look at the fundamental techniques that power modern generative, segmentation models and much more.

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Vishnu Subramanian
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7 Tips and tricks for transfer learning in Pytorch

Transfer learning has become a key component of modern deep learning, both in the fields of CV and NLP. Read the post to learn how to tweak your neural network to achieve better results from your data.

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Vishnu Subramanian