Data Pre-processing for Deep Learning models (Deep Learning with Keras – Part 2)

Link to Part 1. Dealing with Data Motivation Training deep...

Logistic Regression With A Real-World Example in Python

In this tutorial, You’ll learn Logistic Regression. Here you’ll know what exactly is Logistic Regression and you'll also see an Example...

Deep Learning with Keras Tutorial – Part 1

About this series This post is the first part of Deep Learning with Keras series. This series aims to introduce...

Getting Started With Pytorch In Google Collab With Free GPU

In this post, we will discuss the basics of pytorch by using the google colab gpu

How to Connect Google Colab with Google Drive

In this tutorial, you’ll learn how to connect your Google Colab with Google Drive to build some Deep Learning model on Google...

List of Data Science and Machine Learning GitHub Repositories to Try in 2019

Here is the list of selected Data Science and Machine Learning GitHub Repositories to Try in 2019 Paper with...

Top Artificial Intelligence Books to Read in 2019

1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory...

How Generative Adversarial Networks (GANs) work?

Generative Adversarial Networks were first introduced in 2014 in a research paper. They have also been called  “the most interesting idea in...

Combining Deep and Reinforcement learning

Machine learning fundamentally involves learning from the data and making conclusions/decisions about a given problem. It utilizes the following popular approaches.

Principal component analysis (PCA) using R

Principal component analysis (PCA) is a statistical analysis technique that uses an orthogonal transformation to convert a set of observations of possibly correlated variables...