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11 min read
Tanul Singh

NLP鈥檚 State completely changed when in 2018, researchers from Google open-sourced BERT (Bi-Directional Encoder Representation From Transformers).

31 min read
Atharva Ingle

Natural Language Processing is one of the fastest-growing fields in Deep Learning. NLP has completely changed since the inception of Transformers. Later on, variants of Transformer architecture where-in only the encoder part was used (BERT) cracked the transfer learning game in NLP. Now, you can download a pre-trained model from the internet which is already trained on huge amounts of data and has the knowledge of language and use it for your downstream tasks with a bit of fine-tuning.

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

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.

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.

8 min read
Tanul Singh

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.

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

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.

9 min read
Nischay Dhankhar

Introduction

In this competition, we will be predicting engagement with a shelter pet's profile based on the photograph for that profile. Along with the image of each pet, we are also provided Metadata for them that consists of different features like focus, eyes, etc. We aim to somehow utilize both images as well as tabular data in the best possible way to minimize the error rate.

5 min read
Nischay Dhankhar

Introduction

In this competition, we will be predicting answers to questions in Hindi and Tamil. The answers are drawn directly from a limited context given to us for each sample. The competition is diverse and unique compared to other competitions currently held on Kaggle focusing on Multilingual Natural Language Understanding (NLU), which makes it difficult and exciting to work with. Hence, the task of this competition is to build a robust model in which you have to generate answers to the questions about some Hindi/Tamil Wikipedia articles provided.

7 min read
Poonam Ligade

Why it is important to understand ResNet?

ResNets are the backbone behind most of the modern computer vision architectures. For a lot of common problems in computer vision, the go-to architecture is resnet 34. Most of the modern CNN architectures like ResNext, DenseNet are different variants to original resnet architecture. In different subfields of computer vision like object detection, image segmentation resnet plays an important role as a pre-trained backbone.

8 min read
Vishnu Subramanian

In the last several weeks I saw a lot of posts showcasing demos/products on how to use AI algorithms for recognizing people who are not wearing masks. In this post, I will take you through a very simple approach to show how can you build one yourself and also end by asking few questions that can help in building a product that can be used for production. We will be using PyTorch for this task, but the steps would remain almost the same if you are trying to achieve it using another framework like TensorFlow, Keras, or MxNet.

To build any AI algorithm, the most common approach is to

  1. Prepare a labeled dataset.
  2. Choose an architecture that suits your needs. Preferably pre-trained based on your use-case.
  3. Train the model and test the model.

12 min read
Vishnu Subramanian

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Transfer learning has become a key component of modern deep learning, both in the fields of CV and NLP. In this post, we will look at how to apply transfer learning for a Computer Vision classification problem. Along the way I will be showing you how to tweak your neural network to achieve better results. We are using PyTorch for this, but the techniques that we learn can be applied across other frameworks too.

13 min read
Vishnu Subramanian

When working on Deep learning projects getting the right pipeline starting from data processing to creating predictions is a nontrivial task. In the last few years, several frameworks were built on top of popular deep learning frameworks like TensorFlow and PyTorch to accelerate building these pipelines. In this blog, we will explore how we can use one of the popular frameworks fastai2 which is currently in the early release but the high-level API is stable for us to use.