A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists. Originally from KDnuggets https://ift.tt/378bs5n source https://365datascience.weebly.com/the-best-data-science-blog-2020/cartoon-thanksgiving-and-turkey-data-science
Category Archives: Data Science News
Better data apps with Streamlits new layout options
Introducing new layout primitives – including columns, containers and expanders! Originally from KDnuggets https://ift.tt/39eD6Al source https://365datascience.weebly.com/the-best-data-science-blog-2020/better-data-apps-with-streamlits-new-layout-options
Essential Math for Data Science: Integrals And Area Under The Curve
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models. Originally from KDnuggets https://ift.tt/2Ky2tmc source https://365datascience.weebly.com/the-best-data-science-blog-2020/essential-math-for-data-science-integrals-and-area-under-the-curve
Time Series and How to Detect Anomalies in ThemPart II
Time Series and How to Detect Anomalies in Them — Part II Implementation of ARIMA, CNN, and LSTM Hello fellow reader (and hello again if you read the first part of this article series). My name is Artur, and I am the head of the Machine Learning team in Akvelon’s Kazan office and you are about to read theContinue reading “Time Series and How to Detect Anomalies in ThemPart II”
Testing the waters of Bayesian Neural Networks(BNNs)
Intelligence boils down to two things for me -> 1. Acting when certain/necessary. 2. Not acting/staying pensive when uncertain. Point (2.) is what we are going to dive in! Uncertainty is inherent everywhere, nothing is error free. So, it is frankly quite surprising that for most Machine Learning projects, gauging uncertainty isn’t what’s aimed for! Trending AI Articles:Continue reading “Testing the waters of Bayesian Neural Networks(BNNs)”
Using AI to Win in Love: A Step-by-Step Guide
The efficient way to avoid loneliness Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/using-ai-to-win-in-love-a-step-by-step-guide-fd5eb169642a?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/using-ai-to-win-in-love-a-step-by-step-guide
New Course! Data Cleaning and Preprocessing with pandas
Hi! My name is Martin. I’m a Master of Science in Economic and Social Sciences from Bocconi University in Milan, Italy but to all my students, I am also the author and instructor of the Python, SQL, and Integration courses in the 365 Data Science Program. And I am excited to share that we justContinue reading “New Course! Data Cleaning and Preprocessing with pandas”
How to Incorporate Tabular Data with HuggingFace Transformers
In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models. Originally from KDnuggets https://ift.tt/3pZaReM source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-to-incorporate-tabular-data-with-huggingface-transformers
Simple Python Package for Comparing Plotting & Evaluating Regression Models
This package is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying itContinue reading “Simple Python Package for Comparing Plotting & Evaluating Regression Models”
How to evaluate the Machine Learning models?Part 4
How to evaluate the Machine Learning models? — Part 4 This is the fourth part of the metric series where, we will discuss about evaluation of the ML/DL model using metric NLP model are little tricky to evaluate because the output of these model is text/sentence/paragraph. So, we have to check the syntactical, semantic and well as theContinue reading “How to evaluate the Machine Learning models?Part 4”
365 Data Science 