Towards Data Science

It is advisable to ensure you go through at least 10–20% of the content of each paper you have added to the list; this will ensure that you have been exposed to enough of the introductory content within an identified resource and are able to gauge its relevancy accurately. For the more relevant papers/resources identified, … Read more How You Should Read Research Papers According To Andrew Ng (Stanford Deep Learning Lectures)

Not going to lie, Microsoft has been doing some good things in the software development community. I love coding in Visual Studio Code and ONNX has been great if you want to optimize your deep learning models for production. WSL2 allowing you to have access to an entire Linux Kernel is exactly what I’ve been … Read more Cuda on WSL2 for Deep Learning — First Impressions and Benchmarks

pandas is a powerful, flexible and accessible data mining library in Python. It was originally developed at a financial management company. Anyone familiar with the finance sector knows a lot of its data science is actually time series analysis. In fact, the name Pandas came from panel data, which is a special type of time … Read more Pandas essentials for data science

As a person of culture and science, I decided to build a model to identify memes. This problem is far simpler than the Image-Net competition and so a simpler solution is appropriate. I will demonstrate this by comparing the “Meme Vision” framework to ResNet-50 (the winner of Image-Net 2015). Method: Meme Vision framework In a … Read more Meme Vision: the science of classifying memes

If you find a great opportunity for a public dataset, take it. But anecdotally, these are far and few between. It’s less defensible because anyone can use it, and you likely can’t generate additional data points unless the dataset is updated. As a proportion of data in the world, public datasets make up a tiny … Read more Data Over Everything

You should start learning before you do anything else. Don’t listen to critics who say online courses/certifications won’t get you the job. As a beginner, how else will you acquire the relevant knowledge and skills? We start with learning but we won’t stop there. So by all means, keep learning. Keep Learning! (Photo by Tim … Read more Your Data Science Journey Kickstarts Here

A NEW AI LIBRARY FROM MAX TEGMARK’S LAB AT MIT Let’s kick the tires on a brand new library Image by Gerd Altmann from Pixabay (CC0) Table of Contents 1. Introduction2. Code3. Their Example4. Our Own Easy Example5. Symbolic Regression on Noisy Data 1. A New Symbolic Regression Library I recently saw a post on … Read more AI Feynman 2.0: Learning Regression Equations From Data

Going back to our example, you could imagine a model that has as many clusters as there are data points. See, no outliers! But that wouldn’t be a very useful model. All models are wrong, but some are useful. We have to balance the maximum likelihood of our model, L, against the number of model … Read more An Intuitive Explanation of the Bayesian Information Criterion

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below: Machine learning isn’t rocket science, unless you’re doing it at NASA. And if you happen to be doing data science … Read more Data science at NASA

Ways to ensure Data Integrity Photo by Martin Sanchez on Unsplash One of the core functions of any Database Management System is to ensure the integrity of data during its life cycle. Data Integrity, in simple terms, means that data should remain ‘consistent’ and ‘accurate’ as time goes by. Inconsistent or corrupt data will be … Read more Constraints in Oracle

There has been a lack of attention for XSLeaks (cross-site leaks) which result in the leaking of user information source Most developers are familiar and aware of the security vulnerabilities XSS (Cross-site scripting), CSRF (Cross-site request forgery) or SQL Injection, but there has been a lack of attention for XSLeaks (cross-site leaks) which can result … Read more Is your website leaking sensitive information?

How COVID-19 has accelerated a “voice technology moment” that could change communication forever Tokyo. Photo by author. The coronavirus has led to all kinds of innovation: Korea invented drive-through testing, Lithuania invented 3D-printed hands-free door handles, and at this point, nearly everyone has shifted their meetings and social events to a VoIP solution like Zoom. … Read more The Coronavirus vs Voice Technology in Asia

We will see the ability of GAN to generate new images which makes GANs look a little bit “magic”, at first sight. A generative adversarial network (GAN) is a class of machine learning frameworks conceived in 2014 by Ian Goodfellow and his colleagues. Two neural networks (Generator and Discriminator) compete with each other like in … Read more Getting Started with GANs Using PyTorch

Pooling Mechanisms Deep Learning at FAU. Image under CC BY 4.0 from the Deep Learning Lecture These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created … Read more Activations, Convolutions, and Pooling — Part 4

Good, Bad Or Ugly In this data-driven age, organizations are looking to leverage data to enhance business efficiency and effectiveness. The decision on all levels are made with the use of BI and Advanced Analytics tools, better the data better outputs those tools provide and that leads to the creation of opportunities, generating more revenue … Read more 10 Reasons Why You Need Reliable Data Quality

Software Engineering for Data Science A widely used practice in software engineering deserves its own flavor in our field Photo by Yancy Min on Unsplash As Data Science and Machine learning get wider industry adoption, practitioners realize that deploying data products comes with a high (and often unexpected) maintenance cost. As Sculley and co-authors argue … Read more Rethinking Continuous Integration for Data Science

What if our memory was a storage device? How much easier the learning process would be. But the reality is to become an excellent professional in something you need to go through the thorny path. You learn, you forget, you make mistakes, you learn again, absorb new things, and thus you form a picture of … Read more Roadmap to Machine Learning: Key Concepts Explained

Photo by Waranont (Joe) on Unsplash How to build a binary image classifier using fully-connected layers in TensorFlow/Keras This is a short introduction to computer vision — namely, how to build a binary image classifier using only fully-connected layers in TensorFlow/Keras, geared mainly towards new users. This easy-to-follow tutorial is broken down into 3 sections: … Read more 10 Minutes to Building a Fully-Connected Binary Image Classifier in TensorFlow

Learn to Implement Multiple Linear Regression with Python programming. In the previous story, I had given a brief of Linear Regression and showed how to perform Simple Linear Regression. In Simple Linear Regression, we had one dependent variable (y) and one independent variable (x). What if the marks of the student depended on two or … Read more Machine Learning Basics: Multiple Linear Regression

What if we used these technologies to actually make call center agents’ lives better? I don’t mean coaching them to do a better job. “Feedback overload” is already a recognized problem in call centers. I mean helping them cope with the fact that their job is emotionally draining. Remember how frustrating it was the last … Read more How Can AI Boost Call Center Moral?

Generalizing Word2Vec away from word embeddings “Subreddit Embedding” and the 100 closest subreddits to /r/nba A common problem in ML, natural language processing (NLP), and AI at large surrounds representing objects in a way computers can process. And since computers understand numbers — which we have a common language for comparing, combining and manipulating — … Read more Anything2Vec: Mapping Reddit into Vector Spaces

Photo by Aw Creative on Unsplash The authors start the paper with a very interesting analogy to explain the notion that the requirements for the training & inference could be very different. The analogy given is that of a larva and its adult form and the fact the requirements of nourishments for the two forms … Read more [Paper Summary] Distilling the Knowledge in a Neural Network

The network latency is one of the more crucial aspects of deploying a deep network into a production environment. Most real-world applications require blazingly fast inference time, varying anywhere from a few milliseconds to one second. But the task of correctly and meaningfully measuring the inference time, or latency, of a neural network, requires profound … Read more The Correct Way to Measure Inference Time of Deep Neural Networks

Let’s break this function down one by one. The first component, face classification, simply penalizes the model for saying that there is a face at a location, while no face exists in the image. “Face box regression” is a fancy term for the distance between the bounding box coordinates of the predicted face and the … Read more Is Facial Recognition Technology Racist? State of the Art algorithms explained

We cannot use comparison operators=,<,>,<>to test for NULL values. Instead, we have to use IS NULL and IS NOT NULL predicates. IS NULL: Return rows that contain NULL values Syntax: expression IS NULL SELECT ID, Student,Email1,Email2FROM tblSouthParkWHERE Email1 IS NULL AND Email2 IS NULLORDER BY ID The above query yields all records where both Email1 … Read more A Complete Beginner’s Guide to Deal with NULL Values in SQL

Recent attempts to predict criminality from facial features recall a long tradition of unethical and racist pseudoscience Source: Wikimedia Commons A recent paper about to be published by Harrisburg University caused quite a stir earlier this month. Titled “A Deep Neural Network Model to Predict Criminality Using Image Processing,” the paper promised: With 80 percent … Read more AI pseudoscience and scientific racism

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here. In this … Read more Coronavirus: Which country got it right?

Beyond the already complex challenge of implementing AI, some companies have started analyzing the possible benefits of building an AI Decentralized Autonomous Organizations (AI DAOs). During my latest mission, I had to help create new business models, identify the right AI approach, and create a roadmap for the creation of several AI DAOs proof of … Read more Why Building an AI Decentralized Autonomous Organization (AI DAO)

Data visualization tools such as Tableau are loved and used because of how simple they make it to show correlations in large datasets. The exact reason they are used is also their biggest flaw. It’s too easy to simply click buttons until you find something which looks acceptable. Lets look at some examples. I’ve recreated … Read more My Tableau dashboards sucked – until I started drawing them

y=α+βx would give the predicted values and we calculate the values of α & β from the above formula where β is the slope and α is the y-intercept. The goal of the simple linear regression is to create a linear model that minimizes the sum of squares of the residuals(error). An interesting fact about … Read more ANOVA for Regression

import streamlit as stimport pandas as pdimport plotly.express as pximport pydeck as pdkimport numpy as np#Load and Cache the [email protected](persist=True)def getmedata():url = ‘https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv’df = pd.read_csv(url, delimiter=’,’, header=’infer’)df.rename(index=lambda x: df.at[x, ‘Country/Region’], inplace=True)dft = df.loc[df[‘Province/State’].isnull()]dft = dft.transpose()dft = dft.drop([‘Province/State’, ‘Country/Region’, ‘Lat’, ‘Long’])dft.index = pd.to_datetime(dft.index)return(dft, df)df1 = getmedata()[0]st.title(‘Building a Data Dashboard with Streamlit’)st.subheader(‘while exploring COVID-19 data’)#####In Scope Countriescountrylist … Read more Learn How to Create Web Data Apps in Python

Insights from Open Data and Machine Learning I know what you are thinking: Wars are rare and complicated events, one can’t expect to take into account their entire complexity. And you are right, they spring from an intricate array of political, economic, and historical reasons without forgetting the thick coat of randomness, thus they should … Read more Predicting Future Wars

This article is Part 1 of the series “Winning in Analytics!”. Let’s look at key enablers, to scale your AI initiatives with success. Photo by Tim Mossholder on Unsplash Dear AI Enthusiasts, we love to realize the full potential of our data! We would love to see our analytics proof of concepts achieve reality! But … Read more Industrialize Analytics — How do we get there?

Theory, assumptions, problems, and solutions for practitioners Photo by Pixabay from Pexels The equation offered by Black and Scholes (1973) is the standard theoretical pricing model for European options. The keyword being theoretical as the Black-Scholes model makes some key assumptions that are immediately violated in practice. Key model assumptions: No transaction costs No arbitrage … Read more Black-Scholes Option Pricing is Wrong

This post is for all those data science aficionados out there who recently jumped on to the machine learning bandwagon. Whether you studied data science in college or are autodidactic, most aspiring data scientists get a reality check when trying their hand on a machine learning project in a practical setting. I struggled with the … Read more How to Avoid Potential Machine Learning Pitfalls