Aishwarya Verma

Guide to VISSL: Vision Library for Self-Supervised Learning

VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research. This framework is based on PyTorch. The key idea of this library is to speed up the self-supervised learning process from...

Guide to pgmpy: Probabilistic Graphical Models with Python Code

Probabilistic Graphical Models(PGM) are a very solid way of representing joint probability distributions on a set of random variables. It allows users to do inferences in a computationally efficient way. PGM makes use...

Guide to MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Class Imbalance is quite a known problem in datasets, and Imbalanced Learning is used to handle this problem by learning an unbiased model in the data. Existing approaches include Resampling, Reweighting, Ensemble...

Guide to GANSpace: Discovering Interpretable GAN Control

Over the years, the image quality produced by GAN models has improved at a tremendous rate but the interpretability and edibility of the generated output image are not at the same pace with it. We are often in a dilemma...

Guide to Differentiable Augmentation for Data-Efficient GAN Training

Training a GAN model often requires a large collection of the annotated dataset(FFHQ dataset, Imagenet Dataset). Making these large-scale datasets can take months or even years and sometimes it is also not feasible to...

Now GANs Are Being Used For Drug Discovery: Complete Guide To Quantum GAN With Python Code

Developing a new drug is a challenging task as it requires to go through a multi-step process. From developing a target discovery to its clinical trials, this process might take a considerable amount of time. And after...

Guide to Apple’s Equivariant Neural Rendering: Generating 3D scenes with no 3D supervision

Apple has introduced a new method for generating 3D scenes called Equivariant Neural Rendering. This method requires no 3D supervision but only images and their relative positions to learn equivariant scene...

Guide to Torchmeta- A Meta-Learning library for PyTorch

Torchmeta is an open-source meta-learning library built on top of Pytorch deep learning framework. The objective of Torchmeta is to allow easy benchmarking and reproduce the existing pipelines/ research work in...

Hands-on Guide to PyTorch 3D – A Library for Deep Learning with 3D Data

Facebook AI’s PyTorch 3D is a python library to deal with 3D data in deep learning. It is based on PyTorch tensors and highly modular, flexible, efficient and optimized framework, which makes it easier for researchers...

CrypTen – A Research Tool for Secure and Privacy – Preserving Machine Learning in Pytorch

Facebook’s Pytorch had created a huge buzz in the market when it was released five years ago. Now, it is not only the most preferred frameworks for Machine Learning and Deep Learning models but also one of the most...

Hands-On Python Guide to Optuna – A New Hyperparameter Optimization Tool

Hyperparameter Optimization is getting deeper and deeper as the complexity in deep learning models increases. Many handy tools have been developed to tune the parameters like HyperOpt, SMAC, Spearmint, etc. However,...

Hands-On Python Guide to LAMA – An Automatic ML Model Creation Framework

LightAutoML (LAMA) is an open-source python framework developed under Sberbak AI Lab AutoML group. It is focussed at Automated Machine Learning providing end-to-end solutions for ML tasks. It is a light-weight and...

Guide To AI Explainability 360: An Open Source Toolkit By IBM

In our previous article, we detailed out the need for Trusted AI and discussed one of IBM Research Trusted AI toolkit called AIF360. I recommend you to read this article first for better understanding. In this article,...

Guide to AI Fairness 360: An Open Source Toolkit for Detection And Mitigation of Bias in ML Models

As time passed, AI and ML have become more integral parts of our day-to-day life. People in today’s world are exposed to this new wave of technology in one way or another without even knowing it. Some of the most...

Hands-On Guide To Weights and Biases (Wandb) | With Python Implementation

Everything in Data Science begins with the given data to experiment with and a big amount of time is usually spent on data modeling; tracking all the results and visualizing all the data for every run. Sometimes, this...

Hands-On Guide to TadGAN (With Python Codes)

Anomaly Detection techniques have been widely used in data science and now with the rapid increase in temporal data, there has been a huge surge of researchers who are developing new algorithms dealing with outliers...

Complete Guide To AutoGL -The Latest AutoML Framework For Graph Datasets

Creating algorithms is difficult and time-consuming. This specific problem has inspired researchers to develop some productivity tools to help young members in this domain. This has given birth to a revolutionary field...

Hands-on Guide to Reformer – The Efficient Transformer

Ever since The Transformers come into the picture, a new surge of developing efficient sequence models can be seen. The dependency on the surrounding context plays a key role in it. Keeping in mind that the context window used by transformers encompasses thousands of words, its application extends to languages and covers music notes and images. However, expanding the Transformers context window further may result in failure. The...