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Let’s dive into exploring the use case of Sankey Charts in this series of Advanced Visualisation Techniques for Data Science. Data Science has been gaining momentum over the past couple of years. It’s undoubtedly one of the hottest fields in today’s time. In this article, I am going to discuss about an essential part of … Read more The tale of Ultra Modern Visualizations – Sankey chart

First things first — the data. We’ll store some dummy data in the JSON format. I’ve named mine sales.json and it looks like this: [{“Period”: “Q1–2020”, “Amount”: 1000000},{“Period”: “Q2–2020”, “Amount”: 875000},{“Period”: “Q3–2020”, “Amount”: 920000},{“Period”: “Q4–2020”, “Amount”: 400000}] And that’s it for the data. Next, we need the HTML file. Don’t worry if you don’t know … Read more D3.js — How to Make a Beautiful Bar Chart With The Most Powerful Visualization Library

You should now have a higher quality understanding of how the FiveThirtyEight model works. It is a simple but effective model to predict the outcome of games, win probabilities, and their point spreads. However, the model can be improved even further, and improve it I did! I have found other adjustments that the people at … Read more Improving a Famous NFL Prediction Model

The amount of time you need to prepare for the exam will vary significantly. If you’re a senior Cloud engineer who’s transferring from AWS to Azure, you probably don’t need more than a day at tops. If you’re wondering why Microsoft offers a certification covering those fluffy white things in the sky, give yourself a … Read more How to Fail The Azure Fundamentals Certification

Open set recognition Supervised learning, then, is not necessarily enough to cope with the real world. The real world is simply too messy, and it tends to throw new things at us. Here’s a clever way to test this idea: train a machine learning model on MNIST data, but only show it the digits 0,1,2,3,4,5 … Read more Supervised learning is not enough

You may have noticed that even a powerful machine like the Nvidia DGX is not fast enough to train a deep learning model quick enough. Not mentioning the long wait time just to copy data into the DGX. Datasets are getting larger, GPUs are disaggregated from storage, workers with GPUs need to coordinate for model … Read more Distributed Deep Learning Training with Horovod on Kubernetes

Sound is a wave that results from the back and forth vibration of the medium particles through which the sound wave moves. These sound waves consist of a repeating pattern of high-pressure and low-pressure regions. They are also referred to as pressure waves. An example of a sine wave. When we hear something, our brain … Read more Understanding Signals. It’s not that complicated.

Which opportunities could the data show? Introduction The Background Brazil is known for Rio de Janeiro or São Paulo. But one of the most southern states os Brazil has a peculiar capital on an island. Or most of the capital is on this island. I’m talking about Florianópolis. Different from the idyllic idea of an … Read more Best places for new businesses in Florianópolis, Brazil (North Shore): a Foursquare data analysis

When starting a new modeling project, it is important to start with EDA in order to understand the dataset. In this case, the credit card fraud dataset from Kaggle contains 284,807 rows with 31 columns. This particular dataset contains no nulls, but note that this may not be the case when dealing with datasets in … Read more How to Build a Machine Learning Model to Identify Credit Card Fraud in 5 Steps

Vulnerabilities in open source software, programming languages or projects is a big thing as a single exploit could cause a lot of chaos and lead to the loss of thousands of dollars for big organizations. A lot of companies have been paying attention to vulnerabilities in software, dependencies, and languages they use in powering their … Read more Building a Command Line Application to Check For Open Source Vulnerabilities

Graph Queries and why they are popular Because graph databases readily store predicate information for each relationship in the schema of the database, the database can be queried in a language which exploits the predicate information and which makes for queries which are easy to read. The following is an example of a graph query … Read more What is a graph database?

Explainable AI is a big thing nowadays. In ALEX: Active Learning based Enhancement of a Model’s Explainability, the authors use a novel kind of query strategy: prioritization of instances that are “difficult to explain”. (They use the SHAP framework to determine the latter.) Their goal is to arrive at a classifier that is optimized for … Read more Active and Semi-Supervised machine learning: Aug 31 — Sep 11

Analyzing and categorizing poetry to prepare for content-based recommendation Image by abi ismail on Unsplash Within written media, poetry is often regarded as enigmatic, frivolous, or too niche. As a result, poems (even by established poets) are often overlooked by larger publishers and literature-focused websites alike. (The anti-capitalist nature of poetry may play a role … Read more Predicting Poetic Movements

Analysing data from Facebook interactions and messages with my girlfriend I recently downloaded all the data Facebook has about me. There were many interesting (read: cringeworthy) things I found, but one area I was particularly keen to examine is how the data reflects the progression of my relationship with my girlfriend. In this article, I’m … Read more 4.5 years of a relationship, in Facebook activity

A graph is an interesting type of data. We could’ve thought that we can make predictions and train the model in the same way as with “normal” data. Surprisingly, machine learning tasks are defined much differently on graphs and we can categorize it into 4 types: node classification, link prediction, learning over the whole graph, … Read more Machine Learning Tasks on Graphs

I will be using the pre-trained Anime StyleGAN2 by Aaron Gokaslan so that we can load the model straight away and generate the anime faces. So, open your Jupyter notebook or Google Colab, and let’s start coding. Note: You can refer to my Colab notebook if you are stuck So first of all, we should … Read more Generating Anime Characters with StyleGAN2

With PyTorch and Deep Java Library QuickDraw Dataset (Sourcehttps://github.com/googlecreativelab/quickdraw-dataset/blob/master/preview.jpg) The objective of a doodle draw game is to race to create a drawing of a particular item, like a house or a cat, as fast as you can. While the drawing part is simple, before the advent of deep learning it would have been impossible … Read more Learn to Create a Doodle Draw Game on Android

As developers, regardless of our specialty, whether it being data science, front end, or back end, we spend more than 75% of our time reading code written by others. That task can be such a demanding task. That being said, the ability to read others’ code efficiently is one of the skills that could make … Read more The Most Efficient Way to Read Code Written by Someone Else

Image via Unsplash The factorization problem is one of those problems that everyone can understand and most of us feel that it is their opportunity: the divine breach to mathematics hole of fame: Let N, an odd integer a relatively big binary representation ( e.g. hundreds of bits) and p,q prime numbers such that: 1. … Read more Quantum Factorization

The problem with choosing an optimizer is that, due to the no-free-lunch theorem, there is no single optimizer to rule them all; as a matter of fact, the performance of an optimizer is highly dependent on the setting. So, the central question that arises is: Which optimizer suits the characteristics of my project the best? … Read more Which Optimizer Should I Use in my Machine Learning Project?

Create differentially private, synthetic versions of datasets; while meeting compliance requirements to keep sensitive data within your approved environment. Whether your concern is HIPAA for Healthcare, PCI for the financial industry, or GDPR or CCPA for protecting consumer data, being able to get started building without needing a data processing agreement (DPA) in place to … Read more Create high quality synthetic data in your cloud with Gretel.ai and Python

Now that we have added public query datasets, we can query them. Let us see that in the next section. Running GIS Queries with BigQuery You can now run standard SQL queries to explore public datasets. However, since these datasets are usually large, you can run select statements with limiting the number of rows to … Read more A beginner’s Guide to Google’s BigQuery GIS

We now live longer than ever. Yet, we are not necessarily living healthier anymore: with a rapidly aging population, people are experiencing a continuous growth of chronic diseases such as cancer, metabolic, neurological, and heart disorders. This drives healthcare costs through the roof and puts a significant strain on the public health systems [1]. A … Read more AI-designed “hyperfoods” can possibly help prevent cancer

Calculating the total number of trainable parameters in the feed-forward neural network by hand Machine learning is solving such a large number of sophisticated problems today that it seems like magic. But there isn’t any magic in machine learning rather it has a strong mathematical and statistical foundation. While trying to understand the important and … Read more Number of Parameters in Feed-Forward Neural Network

Image from Pixabay Introduction Twitch is a live video streaming platform that allows users to watch and broadcast live streamed or pre-recorded videos of the broadcaster’s video game gameplay. The platform is owned and operated by Twitch Interactive, a subsidiary of Amazon. Founded in 2011 as an offspring of the “stream anything platform”, Justin.tv, its … Read more The Twitch Data Scientist Interview

Cosine similarity has its place in several applications and algorithms. From the world of computer vision to data mining, there is lots of usefulness to comparing a similarity measurement between two vectors represented in a higher-dimensional space. Let’s go through a couple of scenarios and applications where the cosine similarity measure is leveraged. 1. Document … Read more Understanding Cosine Similarity And Its Application

Through this blog you will review Knowledge Distillation (KD) and six follow-up papers. In 2012, AlexNet outperformed all the existing models on the ImageNet data. Neural networks were about to see major adoption. By 2015, many state of the arts were broken. The trend was to use neural networks on any use case you could … Read more Knowledge Distillation — A Survey Through Time

You have a binary classification problem at hand. Let’s denote the two classes in the target variable as ‘Negative’ and ‘Positive’. You have the dataset to be used to develop the classifier, have performed exploratory data analysis, feature engineering and have come to a conclusion what model should be trained. You have divided your data … Read more Confusion Matrix — What is it?

Unraveling the confusion behind the confusion matrix Image by Jon Tyson, from Unsplash Accuracy performance metrics can be decisive when dealing with imbalanced data. In this blog, we will learn about the Confusion matrix and its associated terms, which looks confusing but are trivial. The confusion matrix, precision, recall, and F1 score gives better intuition … Read more Performance Metrics: Confusion matrix, Precision, Recall, and F1 Score

Design Before we go ahead and start coding one should spend some time to create a rough layout. The way I go about is to draw it on a piece of paper — where charts should go, where would the drop-downs be? Will the charts be connected to the buttons?… so onIn case you do … Read more Build Quick and Beautiful Apps using Streamlit

Poisson distribution helps us to predict the probability of a specific event occurs within a time interval. Main Characteristics of a Poisson Distribution: The number of changes occurring in nonoverlapping intervals is independent. The probability of exactly one change occurring in a sufficiently short interval of length h is approximately λh, where λ>0. The probability … Read more Understanding and Choosing the Right Probability Distributions with Examples

This is the folder layout I tend to use at the beginning of any ML project. This layout is open to extension (such as adding a tests folder, deploy folder, etc) as soon as the project needs to grow up. project # project root├── data # data files├── models # machine learning models├── notebooks # … Read more 4 techniques to enhance your Research in Machine Learning projects

Make Data Science Fun again If you want to learn data science, there are many resources out there for you to learn data science. Since the demand for learning data science is high, there are thousands of data science courses, books, libraries to help beginners to get into the field. This is wonderful because you … Read more How not to be Overwhelmed with Data Science

It’s time to adopt drag and drop design tools to train models Photo by UX Store on Unsplash At the turn of this decade, there is a surge of no-code AI platforms. More and more businesses are looking to leverage the power of artificial intelligence to build smarter software-based products. But execution becomes an obstacle … Read more Top 8 “No-Code” Machine Learning Platforms You Should Use In 2020

Photo by Jordan Harrison from Pexels Speed-ups of 50–60% with just a handful of new lines of code. TLDR: the torch.cuda.amp mixed-precision training module in PyTorch 1.6 delivers on its promise, delivering speed-ups of 50–60% in large model training jobs with just a handful of new lines of code. One of the most exciting additions … Read more A developer-friendly introduction to mixed precision training with PyTorch

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It was one of the hardest problems for computers to translate from one language to another with a simple rule-based system … Read more A Comprehensive Guide to Neural Machine Translation using Seq2Sequence Modelling using PyTorch.

In the third part, we will focus on building the templates for the User app we defined in the last tutorial. Image by Freepik So, in the second part, we defined all the backend part of the Users App, like we defined all the URLs, views, models, forms and registered our models with the admin … Read more Build a Social Media Website with Django – Part 3

You can follow along using the code available in my GitHub. Step 1: Choosing a k value Choice of K has a drastic impact on the results we obtain from kNN. Better choose an odd number. Step 2: Calculating Distance The next step is to calculate distance between two rows in a dataset. Problem or … Read more Build kNN from scratch – Python

Introduced a new aspect-based sentiment analysis (ABSA) dataset known as Multi-Aspect Multi-Sentiment (MAMS), where each sentence contains at least two different aspects and two difference sentiment. The proposed MAMS dataset could solve the common issue of existing ABSA dataset, where most sentences contain the same sentiment for different aspects, degenerating ABSA to sentence-level sentiment analysis. … Read more Day 112 of #NLP365: NLP Papers Summary — A Challenge Dataset and Effective Models for Aspect-Based…