• ## Bayesian Sequential Learning

In my previous article, I discussed how we can use Bayesian approach in estimating the parameters of the model. The process revolves around solving the following conditional probability, popularly known as the Bayes’ Theorem: $$$\mathbb{P}(\mathbf{w}|\mathbf{y})=\frac{\mathbb{P}(\mathbf{w})\mathbb{P}(\mathbf{y}|\mathbf{w})}{\mathbb{P}(\mathbf{y})},$$$ where $\mathbb{P}(\mathbf{w})$ is the a priori (prior distribution) for the objective parameters, $\mathbb{P}(\mathbf{y}|\mathbf{w})$...
• ## TensorFlow 2.0: Building Simple Classifier using Low Level APIs

At the end of my comparison — TensorFlow 1.14 Keras’ API versus Julia’s Flux.jl and Knet.jl high level APIs — I indicated some future write-ups I plan to do, one of which is to compare (obviously) on the low level APIs. However, with the release of the much anticipated TensorFlow...
• ## Model Productization: Crafting a Web Application for Iris Classifier

Any successful data science project must end with productization. This is the stage where trained models are deployed as application that can be easily accessed by the end users. The application can either be part of already existing system, or it could also be a standalone application working in the...
• ## Interfacing with Relational Database using MySQL.jl and PyMySQL

Prior to the advent of computing, relational database can be thought of log books typically used for inventory and visitor’s time-in time-out. These books contain rows that define the item/transaction, and columns describing the features of each row. Indeed, these are the core attributes of any relational database. Unlike spreadsheets,...