If you have not heard about Transformers in recent times in the field of NLP(Natural Language Processing) or Artificial Intelligence, then you are probably living under a rock. There has been an array of Transformer based architectures such as BERT, SpanBERT, Transformer-XL, XLNet, GPT-2, etc getting released frequently for the past couple of years. The OpenAI’s GPT-3 had taken the internet by storm with its ability to perform extremely well on tasks such as Q&A, Comprehension, even Programming(the best part was where it added the comments). Check out here to see what all GPT-3 can do.

But all of this…


Predicting the future has been one of the most fascinating subjects that have been in practice and have evolved over a period of time. Especially in this information age, the development of modern computing and the advancement in Artificial Intelligence has only made it more interesting than ever. Sequence-to-Sequence modeling has been one of the prime research areas where the model learns observations captured over a period of time and able to generate such patterns given a scenario. Though statistical methods have been around for a long period of time the latest advancements in Neural Networks especially RNNs are stealing…


We all know that RNNs are good at sequence modeling. The idea of Recurrence is quite intuitive and it justified the same when applied on various tasks such as Timeseries Analysis and NLP. We have not thought about the Convolutions in a sequence modeling problem initially because we were obsessed with how the CNNs failed to capture the temporal relations but the spatial relations between features, the very quality which had made the CNNs the favorites in terms of Image Processing over the years. But there is a beautiful relationship between Sequence Modeling and Convolutions which we are going to…


In applications like autonomous navigation and augmented reality you might need more than one input to analyze the environment to assess the conditions precisely and take action. For example, an autonomous vehicle might need to detect pedestrians and their depth on its way in order to adjust the speed, the timing of the brake, etc. We can take YOLO v3 and train it to detect passengers, sign-boards, etc, and take a network like MiDaS and use it to detect depth. …

saravana alagar

Data Scientist, Deep Learning Researcher and Robotics practitioner

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