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

Tree-based Feature Importance Machine learning model such as random forests is typically treated as a black-box. Why? A forest consists of a large number of deep trees, where each tree is trained on bagged data using a random selection of features. To gaining a full understanding by examining each tree would close to impossible. For … Read more Machine Learning Explainability Introduction via eli5

Going from floats to integers Neural networks are very resource intensive algorithms. They not only incur significant computational costs, they also consume a lot of memory in addition. Even though the commercially available computational resources increase day by day, optimizing the training and inference of deep neural networks is extremely important. If we run our … Read more How to accelerate and compress neural networks with quantization

Model Evaluation Now that all the theory is out of the way, let’s see how these components come together to produce high-quality recommendations on a well-known real-world data set. We’ll train an implicit feedback FM model using the author’s new RankFM package which implements the techniques described above and compare its performance to the popular … Read more Factorization Machines for Item Recommendation with Implicit Feedback Data

How does it feel to be in one of these roles? Find out here. Photo by Christina @ wocintechchat.com [1]. Introduction Data Analyst Data Scientist Summary References After working as both a professional data analyst and data scientist, I thought it would be insightful to highlight the experience of each position along with some key … Read more Would You Rather be a Data Analyst or Data Scientist?

Using the Apriori algorithm to offer product recommendation, product placement, pricing and bundling strategies Imagine if we could understand what our customers’ next purchase could be! Imagine if we could find patterns in purchase behaviour and use it to our advantage! The key to the future is in history! Market Basket Analysis helps retailers identify … Read more Product Placement, Pricing and Promotion Strategies with Association Rule Learning

The neonatal intensive care unit (NICU) is an environment in which life-changing decisions are made. Neonatologists use information from a variety of sources to build up a picture of a newborn’s condition to ensure they are receiving the right medical care. These highly trained specialists use their judgement in tandem with a constant stream of … Read more Machine Learning for Neonatal Intensive Care

In this post, I will show you how to build a simple face detector using Python. Building a program that detects faces is a very nice project to get started with computer vision. In a previous post, I showed how to recognize text in an image, it is a great way to practice python in … Read more Simple Face Detection in Python

Image by emmaws4s from Pixabay Note — There is also a YouTube video explaining this paper The paper begins by making a case that often wide & deep models require a huge number of multiplications and that results in high memory and computing demands. Because of this even if the network is a top-performing model … Read more [Knowledge Distillation] FitNets : Hints For Thin Deep Nets

Image by Qimono from Pixabay (CC0) Back in 1958, Han Peter Luhn, a researcher at IBM, initiated the concept of Business Intelligence (BI), using the definition from Webster’s Dictionary: to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal. Given its definition, Business Intelligence is indeed … Read more 6 Key Areas of Business Intelligence in the New Era

In this article, we’ll explore the differences between data scientists as Decision Support, Advisor and Integrated Partner One of the best and worst parts of being a data scientist is the ambiguity that the role can often entail. Since data science is a relatively new function, the mandate and objectives aren’t always clear. This often … Read more The Three Stages of a Data Scientist

WOMEN IN TECHNOLOGY SERIES An Interview with the Head of Operations & Partnerships at Starbutter AI Over the past few years, tech companies and researchers all over the world have been competing to advance the frontiers of artificial intelligence. With the broadening and fast-paced developments in the space of technology, it is clear that utilizing … Read more Jean Alfonso-Decena: Leading Innovation in Conversational AI and Disrupting the Philippine FinTech…

We’re going to bundle this up in a tiny ruby project. Create our directory $ mkdir kiba-etl && cd kiba-etl/ Add the source CSV Create a CSV file with touch phone.csv and paste in the following. id,number1,123.456.78912,2223,303-030-30304,444-444-44445,900-000-000016,#10000000007,#98989898988,800-000-000009,999.999.999910,1.1.1.1.1.1.1.1.1.111,(112)233-445512,(121)212-0000 In a real situation, you might use a service like Twilio to detect if they’re real phone numbers. … Read more Build The World’s Simplest ETL (Extract, Transform, Load) Pipeline in Ruby With Kiba

Using YFinance and Plotly libraries for Stock Data Analysis Photo by Alec Favale on Unsplash In this article, I will explain to you how you can use YFinance a python library aimed to solve the problem of downloading stock data by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! … Read more Downloading Stock Data and Representing it Visually

photo by author I recently had the opportunity to deliver a hands-on workshop on training a Keras deep learning model. This workshop was a follow-on for a session I had done for a local meetup that reviewed the content in my upcoming book for Manning Publications, Deep Learning with Structured Data. After the introductory session … Read more Cocalc vs. Colab — Which Is Better for a Hands-On Workshop?