Building your own datasets in few hours. This post lists downs resources with scripts that can be used to create your own text and image datasets. More stuff will be added to this post in time. Image Datasets

How graph theory, cellular automata and deep learning are silently coming together to shape the future of spatial modelling. — The topic under discussion is very broad, as we will need to discuss the entire scope of spatial analysis before projecting into the future. For the first thing you need to predict the future is knowing the past. So I will narrow down the scope of discussion progressively to the…

Putting invariance be design approach to practice in an image classification setting. Robust Deep Learning This article is a part of series that address Robust Deep Learning. We have talked about model generalization, robustness invariance and causality in details in previous articles. In this article, we provide empirical demonstration of…

Data augmentation is a mainstream tool in Machine Learning, but do we really understand it ? — Let us look at how various sources define data augmentations. Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting…

Learn how to leverage this tool to unlock the true potential of synthetic data for Machine Learning! Domain Randomization Domain randomization is a systematic approach to data generation process that aims to enhance generalization of the machine learning algorithms to new environments. Domain randomization is an approach where one tries to find…

Is all the craze over new and more complex model architectures justified? — Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. In this article we talk about how the AI community has been biased towards putting more effort in the model, and see how it is not always the best approach. …

Upfront causal thinking to design Robust Neural Networks that work well in new environments — As the title employs, we are in the pursuit of robust deep learning. Let us define robust deep learning first. Robust deep learning will result in models that work well in new and unseen environments, which are different from the training distribution. …

Why do Neural networks fail to generalize to new environments, and how can this be fixed? — Many real world data analysis problems exhibit in-variant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning. ~ On the Benefits of Invariance in Neural Networks Most machine learning problems have an invariant structure. Image classification tasks, for example, are usually…

Susceptibility of neural networks to overfit on irrelevant properties and importance of randomizing them. — Robust Deep Learning This article is part of a series that addresses Robust Deep Learning. We have talked about model generalization, robustness invariance and causality in details in previous articles. In this article we look at how Data Augmentations and Domain Randomization help ensure model robustness. …