Recently, I worked on an assignment to analyze the data from bikesharing system to predict its demand. In this post, we will see how the given data can be analyzed using statistical machine learning methods.
In this post we will talk about generating synthetic data from tabular data using Generative adversarial networks(GANs). We will be using the default implementation of CTGAN  model.
Introduction In the last post on GANs we saw how to generate synthetic data on Synthea dataset.
Over the last couple of months, I have been going through a lot of literature about human action recognition using computer vision. In this post, I will share a brief survey of Human Action Recognition.
In this post, we will see how to generate tabular synthetic data using Generative adversarial networks(GANs). The goal is to generate synthetic data that is similar to the actual data in terms of statistics and demographics.
In this notebook I am going to re-implement YOLOV2 as described in the paper YOLO9000: Better, Faster, Stronger. The goal is to replicate the model as described in the paper and train it on the VOC 2012 dataset.
In this notebook I am going to implement YOLOV1 as described in the paper You Only Look Once. The goal is to replicate the model as described in the paper and in the process, understand the nuances of using Keras on a complex problem.