Those working with Neural Networks know how complicated Object Detection techniques can be. It is no wonder there is no straight forward resource for training them. You are always required to convert your data to a COCO-like JSON or some other unwanted format. It is never a plug and play experience. Moreover, no diagram thoroughly explains Faster R-CNN or YOLO as there is for … [Read more...] about Why Is Object Detection So Messy?
Technical Guide
Top Applications of Graph Neural Networks 2021
At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I … [Read more...] about Top Applications of Graph Neural Networks 2021
Time Series Forecasting with Deep Learning and Attention Mechanism
This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting. This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author. Motivation Time Series Forecasting has always been a very important area of research in many domains … [Read more...] about Time Series Forecasting with Deep Learning and Attention Mechanism
Time Series Classification with Deep Learning
This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Classification. This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author. Why Time Series Classification? First of all it’s important to underline why this … [Read more...] about Time Series Classification with Deep Learning
Transformers in Computer Vision
Transformer architecture has achieved state-of-the-art results in many NLP (Natural Language Processing) tasks. One of the main breakthroughs with the Transformer model could be the powerful GPT-3 released in the middle of the year, which has been awarded Best Paper at NeurIPS2020. In Computer Vision, CNNs have become the dominant models for vision tasks … [Read more...] about Transformers in Computer Vision
Building a Complete AI Based Search Engine with Elasticsearch, Kubeflow and Katib
Building search systems is hard. Preparing them to work with machine learning is really hard. Developing a complete search engine framework integrated with AI is really really hard. So let’s make one. ✌️ In this post, we’ll build a search engine from scratch and discuss on how to further optimize results by adding a machine learning layer using Kubeflow and Katib. This … [Read more...] about Building a Complete AI Based Search Engine with Elasticsearch, Kubeflow and Katib
Linguistics Wisdom of NLP Models
This article is authored by Keyur Faldu and Dr. Amit Sheth. This article elaborates on a niche aspect of the broader cover story on “Rise of Modern NLP and the Need of Interpretability!”At Embibe, we focus on developing interpretable and explainable Deep Learning systems, and we survey the current state of the art techniques to answer … [Read more...] about Linguistics Wisdom of NLP Models
Discovering the Encoded Linguistic Knowledge in NLP Models
This article is authored by Keyur Faldu and Dr. Amit Sheth. This article elaborates on a niche aspect of the broader cover story on “Rise of Modern NLP and the Need of Interpretability!”At Embibe, we desiderate answers to the open questions while we build the NLP platform to solve numerous problems for the academic content. Modern NLP models (BERT, GPT, … [Read more...] about Discovering the Encoded Linguistic Knowledge in NLP Models
Deep-Learning Based Object Detection in Crowded Scenes
Object detection in crowded scenes is challenging. When objects gather, they tend to overlap largely with each other, leading to occlusions. Occlusion caused by objects of the same class is called intra-class occlusion, also referred to as crowd occlusion. Object detectors need to determine the locations of different objects in the crowd and accurately delineate their … [Read more...] about Deep-Learning Based Object Detection in Crowded Scenes
Transfer Learning for Time Series Forecasting and Classification
A brief history: ImageNet was first published in 2009 and over the next four years would go on to form the bedrock of most computer vision models. To this day whether you are training a model to detect pneumonia or classify models of cars you will probably start with a model pre-trained on ImageNet or some other large (and general image) dataset. More recently papers … [Read more...] about Transfer Learning for Time Series Forecasting and Classification