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19 min read
Vinayak Nayak

In this post, we shall look at the task of metric learning, and implement the paper Classification is a strong baseline for deep metric learning on the Inshop dataset

  • What is Metric Learning?
  • Framing Image Classification as an Image Retrieval problem
  • Model Architecture
  • Loss Function
  • The Inshop Dataset
  • Implementing the paper on inshop dataset in fastai
    • Building a datablock & dataloader
    • Implementing a custom sampler
    • Implementing a custom metric
    • Training the model
  • Evaluation on unseen data
  • References

4 min read
Vishnu Subramanian

Have you ever wondered 馃 how PyTorch nn.Module works? I was always curious to understand how the internals work too. Recently I was reading Fast.ai's Deep learning for coders book's 19th chapter, where we learn how to build minimal versions of PyTorch and FastAI modules like

  • Dataset, Dataloaders
  • Modules
  • FastAI Learner

This intrigued 馃 me to take a look at the PyTorch source code for nn.Module. The code for nn.Module is 1000+ lines 馃槷. After a few cups of coffee 鈽曗槙, I was able to make sense of what is happening inside. Hopefully, by end of this post, you would have an understanding of what goes insidenn.Module without those cups of coffee 馃槃.

22 min read
Atharva Ingle

One of the least taught skill in machine learning is how to manage and track machine learning experiments effectively. Once you get out of the shell of beginner-level projects and get into some serious projects/research, experiment tracking and management become one of the most crucial parts of your project.

However, no course teaches you how to manage your experiments in-depth, so here I am trying to fill in the gap and share my experience on how I track and manage my experiments effectively for all my projects and Kaggle competitions.

In this post, I would like to share knowledge gained from working on several ML and DL projects.

  • Need for experiment tracking
  • Conventional ways for experiment tracking and configuration management
  • Trackables in a machine learning project
  • Experiment tracking using Weights and Biases
  • Configuration Management with Hydra

5 min read
Vishnu Subramanian

While designing DL modules like a classification head, it is required to calculate the input features. PyTorch Lazy modules comes to the rescue by helping us automate it.

In this post, we will explore how we can use PyTorch Lazy modules to re-write PyTorch models used for

  • Image classifiers
  • Unet

3 min read
Vishnu Subramanian

Time flies. It's been more than a year, from the time we launched. Thanks to all our early adopters for trusting and supporting us. Without your love, feedback and patience we would have not come this far.

In the last few months, we have been working on some key features

  • Managing instance life cycle through Python API
  • Bring your own container
  • Start up script
  • Spot instances
  • Weekly and Monthly prices
  • Live invoice
  • New website, docs and blog page

8 min read
Tanul Singh

longformer

Transformer-Based Models have become the go-to models in about every NLP task since their inception, but when it comes to long documents they suffer from a drawback of limited tokens. Transformer-Based Models are unable to process long sequences due to their self-attention which scales quadratically with the sequence length. Longformer addresses this limitation and proposes an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer鈥檚 attention mechanism is a drop-in replacement for the standard self-attention and combines local windowed attention with task-motivated global attention.

5 min read
Tanul Singh

DeepSpeed With the recent advancements in NLP, we are moving towards solving more and more sophisticated problems like Open Domain Question Answering, Empathy in Dialogue Systems, Multi-Modal Problems, etc but with this, the parameters associated with the models have also been rising and have gone to the scale of billions and even Trillions in the largest model Megatron.

12 min read
Nischay Dhankhar

Effdet

Introduction

EfficientDet model series was introduced by Google Brain Team in 2020 which turns out to be outperforming almost every detection model of similar size in the majority of the tasks. It utilizes several optimizations. Also, many tweaks in the architecture backbone were introduced including the use of a Bi-directional Feature Pyramid Network [BiFPN] and scaling methods which resulted in the better fusion of features.