Month: June 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

import streamlit as stimport pandas as pdimport plotly.express as pximport pydeck as pdkimport numpy as np#Load and Cache the data@st.cache(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

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

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

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

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

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