How to Perform Outlier Detection In Python In Easy Steps For Machine Learning, #1
Towards Data Science
by Bex T.
20h ago
How to Perform Outlier Detection in Python for Machine Learning: Part 1 Earth is an outlier — the theory Image by 0fjd125gk87 from PixabayWhat are outliers? We live on an outlier. Earth is the only hump of rock with life in the Milky Way galaxy. Other planets in our galaxy are inliers or normal data points in a so-called database of stars and planets. There are many definitions of outliers. In simple terms, we define outliers as data points that are significantly different than the majority in a dataset. Outliers are the rare, extreme samples that don't conform or align wit ..read more
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CRPS : Scoring Function for Bayesian ML Models | by Itamar Faran
Towards Data Science
by Itamar Faran
20h ago
CRPS — A Scoring Function for Bayesian Machine Learning Models The Continuous Ranked Probability Score compares distributional predictions to ground-truth values An important part of the machine learning workflow is the model evaluation. The process itself can be considered common knowledge: split the data into train and test sets, train the model on the train set, and evaluate its performance on the test set using a score function. The score function (or metric) is a mapping of the ground truth values and their predictions into a single and comparable value [1]. For example, for continuo ..read more
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Dates and Subqueries in SQL
Towards Data Science
by Michael Grogan
1d ago
Working with dates in SQL Source: Photo by webandi from Pixabay It is often the case that when working with a SQL database, one typically has to work with tables that contain a date column showing the date for each relevant record. However, the ability of SQL to work with dates and yield valuable insights from such data types is often not well understood. Weather Data Example Let us consider the following example. Suppose there exists a weather database with recorded dates and relevant weather information in a table. Here is a snippet of the data: Source: Table (and da ..read more
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Ethics in AI: Potential Root Causes for Biased Algorithms
Towards Data Science
by Jonas Dieckmann
1d ago
An alternative approach to understanding bias in data Image Credits: Pixabay A s the number of data science applications increases, so does the potential for abuse. It is easy to condemn developers or analytics teams for the results of their algorithms, but are they the main culprits? The following article tries to discuss the problem from a different angle and concludes that ethical abuse might be the real problem with data in our society. A better world won’t come about simply because we use data; data has its dark underside. ¹ Biased algorithms Today, discussions of data scie ..read more
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Mixed Integer Linear Programming 1
Towards Data Science
by István Módos
1d ago
Mixed Integer Linear Programming: Introduction How to solve complex constrained optimisation problems having discrete variables Photo by Mitchell Luo on Unsplash Designing and implementing algorithms for complex problems is hard. Fun, but hard. What if I told that you can solve certain optimisation problems using only their mathematical specification? Join me on the journey to the wonderful world of Mixed Integer Linear Programming, which has its applications in nurse rostering, kidney exchange programs, production scheduling, robotic cells energy optimisation, automated Sudoku solving, a ..read more
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Dynamic MIG partitioning in Kubernetes
Towards Data Science
by Michele Zanotti
1d ago
Dynamic MIG Partitioning in Kubernetes Maximize GPU utilization and reduce infrastructure costs. Photo by Growtika on Unsplash To minimize infrastructure expenses, it’s crucial to use GPU accelerators in the most efficient way. One method to achieve this is by dividing the GPU into smaller partitions, called slices, so that containers can request only the strictly necessary resources. Some workloads may only require a minimal amount of the GPU’s compute and memory, so having the ability in Kubernetes to divide a single GPU into multiple slices, which can be requested by individual contain ..read more
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Temporal Differences with Python — First Sample-Based Reinforcement Learning Algorithm
Towards Data Science
by Eligijus Bujokas
1d ago
Temporal Differences with Python: First Sample-Based Reinforcement Learning Algorithm Coding up and understanding the TD(0) algorithm using Python Photo by Kurt Cotoaga on Unsplash This is a continuation article from my previous article: First Steps in the World Of Reinforcement Learning using Python In this article, I want to familiarize the reader with the sample-based algorithm logic in Reinforcement Learning (RL). To do this, we will create a grid world with holes (much like the one in the thumbnail) and let our agent freely traverse our created world. Hopefully, by the ..read more
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Business Document Understanding
Towards Data Science
by Kaveti Naveenkumar
1d ago
Classify document entities using LayoutLM Model Photo by Scott Graham on UnsplashIntroduction Visually-rich Document Understanding (VrDU) aims to extract structured information from the business documents (scanned images or PDFs). This is essential for a variety of applications: Utilising the current invoice template while onboarding to the new financial application Auto-filling customer and product meta data from digital documents (scanned image or PDF) during onboarding to the new financial application Creating a transaction in the financial application using a digital document (s ..read more
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Not All Rainbows and Sunshine: The Darker Side of ChatGPT
Towards Data Science
by Mary Reagan PhD
2d ago
Part 1: The Risks and Ethical Issues Associated with Large Language Models Image credit Fiddler AI with permission If you haven’t heard about ChatGPT, you must be hiding under a very large rock. The viral chatbot, used for natural language processing tasks like text generation, is hitting the news everywhere. OpenAI, the company behind it, was recently in talks to get a valuation of $29 billion¹ and Microsoft may soon invest another $10 billion². ChatGPT is an autoregressive language model that uses deep learning to produce text. It has amazed users by its detailed answers across a variet ..read more
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Overcoming the Limitations of Large Language Models
Towards Data Science
by Janna Lipenkova
2d ago
How to enhance LLMs with human-like cognitive skills How popular LLMs score along human cognitive skills (source: semantic embedding analysis of ca. 400k AI-related online texts since 2021) Disclaimer: This article was written without the support of ChatGPT. In the last couple of years, Large Language Models (LLMs) such as ChatGPT, T5 and LaMDA have developed amazing skills to produce human language. We are quick to attribute intelligence to models and algorithms, but how much of this is emulation, and how much is really reminiscent of the rich language capability of humans? When con ..read more
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