Posts

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 post, we will see how to import data to the Neo4J database from CSV files. Recently, I worked with Graph databases for the first time and was really amazed by the capabilities it offers.

Custom Data Generator, Layer, Loss function and Learning Rate Scheduler In this post, I will demonstrate how you can use custom building blocks for your deep learning model. Specifically, we will see how to use custom data generators, custom Keras layer, custom loss function, and a custom learning rate scheduler.

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.

Tensorflow 2.0 comes with a set of pre-defined ready to use datasets. It is quite easy to use and is often handy when you are just playing around with new models.

In this post, I will explain how to use the Android Paged list with a boundary callback. The idea is to see an example where both the network and the local database is involved.

In this post, I will show you how to quickly add Room support for Room database in your app using Kotlin. There are quite a few articles on how to use Room but most of them are not up to date.

Recently, while working with a large dataset, I wanted to use Google Cloud Storage in my Colab notebook. In this post, I will show how to access these files in Google Colab Notebook.

Principal Component Analysis(PCA) is often used as a data mining technique to reduce the dimensionality of the data. In this post, I will show how you can perform PCA and plot its graphs using MATLAB.