My Deep Learning Workstation Setup

By Sadanand Singh

on  

in Computers

Comments

Lately, a lot of my friends have been asking about my deep learning workstation setup. In this post I am going to describe my hardware, OS, and different packages that I use. In particular, based on the question, I found that the most of the interest have been around managing different python versions, and modules like pytorch/tensorflow libraries etc.

Reading Time: 9 minutes       Read more…

A Practical guide to Autoencoders

By Sadanand Singh

on  

in Machine Learning

Comments

Usually in a conventional neural network, one tries to predict a target vector $y$ from input vectors $x$. In an auto-encoder network, one tries to predict $x$ from $x$. It is trivial to learn a mapping from $x$ to $x$ if the network has no constraints, but if the network is constrained the learning process becomes more interesting. In this article, we are going to take a detailed look at the mathematics of different types of autoencoders (with different constraints) along with a sample implementation of it using Keras, with a tensorflow back-end.

Reading Time: 22 minutes       Read more…

Understanding Boosted Trees Models

By Sadanand Singh

on  

in Machine Learning

Comments

In the previous post, we learned about tree based learning methods - basics of tree based models and the use of bagging to reduce variance. We also looked at one of the most famous learning algorithms based on the idea of bagging- random forests. In this post, we will look into the details of yet another type of tree-based learning algorithms: boosted trees.

Reading Time: 26 minutes       Read more…

Understanding Support Vector Machine via Examples

By Sadanand Singh

on  

in Machine Learning

Comments

In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.

Reading Time: 12 minutes       Read more…

Python Tutorial - Week 2

By Sadanand Singh

on  

in Tutorials

Comments

In the Week 1 we got started with Python. Now that we can interact with python, lets dig deeper into it.

This week we will go over some additional fundamental things common in any program - interactive input from users, adding comments to your code, use of conditional logic i.e. if - else conditions, loops, formatted output with strings and print() statements.

Reading Time: 11 minutes       Read more…