By Zachary Galante — Senior Data Science Student at Bryant University

Artificial Intelligence has been developing so rapidly, that technologies such as the one shown above are not as far off as people believe. It brings along the opportunity to impact humanity in both a positive and negative way. Looking back 20 years, the idea of a fully self driving car seemed crazy but we are closer to that achievement than ever. This is not the only field that could ultimately have a tremendous impact on the human race.

Elon Musk is one of the most innovative people today, he…

Cross validation is one of the things which can be used to make your training of model more reliable with the given data. It is also known as rotation estimation or out of sample testing. You will understand in a while why is it so!

Cross validation or simply CV can also be referred as out of sample testing or rotation sampling. Once you have model which is not generalizing better with test data or in other words, we can say model is overfitting. So, it means you have less data for model to learn and converge. And, solution is…

Python is well-established as the go-to language for data science and machine learning, partially thanks to the open-source ML library PyTorch.

PyTorch’s combination of powerful deep neural network building tools and ease-of-use make it a popular choice for data scientists. As its popularity grows, more and more companies are moving from TensorFlow to PyTorch, making now the best time to get started with PyTorch.

Today, we’ll help understand what makes PyTorch so popular, some basics of using PyTorch, and help you make your first computational models.

Here’s what we’ll cover today:

• What is PyTorch?
• Why use PyTorch?
• PyTorch Basics
• Computation…

In this blog post, we’ll discuss Class Imbalance Problem in machine learning, what causes it and how to overcome it. From my experience of attending interviews, interviewers ask at least one scenario based question on class imbalance, widely being how to handle class imbalance?

When we think of an image, it's almost natural to think of a two-dimensional (2-D) representation right? Well, in reading the previous sentence carefully, ‘almost’ is the keyword.

In a recent paper, Scaling down deep learning, Sam Greydanus introduced a rather cool 1-D image dataset called the MNIST 1-D labelling it as, “A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks.” The benchmark being referred to is the MNIST dataset which is a dataset of hand-written digits that is quite well known within the machine learning (ML) or more generally, the data science space. Unlike the original MNIST…

When GME goes up, the market goes down. Read on!

Disclaimer: This is a short article and does not intent to provide financial advice or to suggest anything whatsoever.

Introduction

Recently there is a lot of noise around GME, reddit and the stock market.

My hypothesis was that there is a significant correlation between GME and S&P 500 time courses of price.

I did a simple correlation analysis and I found that there is a significant (p=0.05) negative correlation (rho= -0.319) between the GME and S&P500 price.

Just for a reminder this is what happened over the past couple of months:

Artificial Intelligence (AI) has already made an impression on higher education. And like businesses in many industries, educators and administrators are cautiously evaluating the pros and cons of AI applications.

There are at least two main areas in the Higher Ed. space where AI can substantially improve business processes and academics. Before we look at some of the ground-breaking technology in the classroom, let’s consider how AI can streamline the business of running a college or university.

AI and Higher Education Workflows

AI can bring about significant changes to how administrative processes are handled at higher Ed institutions. The objective of AI improvements is a…

kNN is one of the simplest yet powerful supervised ML algorithms. It is widely used for classification problems as well as can be used for regression problems. The data-point is classified on the basis of its k Nearest Neighbors, followed by the majority vote of those nearest neighbors; a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.

K-NN is a non-parametric algorithm, which means it does not make any assumptions on underlying data. …