Posts

Analyzing Bike Sharing Demand

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.
Analyzing Bike Sharing Demand

Tabular Synthetic Data Generation using CTGAN

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 [1] model. Introduction In the last post on GANs we saw how to generate synthetic data on Synthea dataset.
Tabular Synthetic Data Generation using CTGAN

Covid-19 Diagnosis using Radiography Images

In this notebook, we will try to classify images from the Covid-19 Radiography Dataset[1] using a pre-trained ResNet-18 network. We will be using the PyTorch library for building our network.
Covid-19 Diagnosis using Radiography Images

Literature Survey: Human Action Recognition

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.
Literature Survey: Human Action Recognition

Generating Tabular Synthetic Data Using GANs

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.
Generating Tabular Synthetic Data Using GANs

Implementing YOLOV2 from scratch using Tensorflow 2.0

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.
Implementing YOLOV2 from scratch using Tensorflow 2.0

Implementing YOLOV1 from scratch using Keras Tensorflow 2.0

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.
Implementing YOLOV1 from scratch using Keras Tensorflow 2.0