Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input tokens. To address this, we propose cheat-sheet ICL, which distills the information from many-shot ICL into a concise textual summary (cheat sheet) used as the context at inference time. Experiments on challenging reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens, and matches retrieval-based ICL without requiring test-time retrieval. These findings demonstrate that cheat-sheet ICL is a practical alternative for leveraging LLMs in downstream tasks.

The need for statistical models of orientations arises in many applications in engineering and computer science. Orientational data appear as sets of angles, unit vectors, rotation matrices or quaternions. In the field of directional statistics, a lot of advances have been made in modelling such types of data. However, only a few of these tools are used in engineering and computer science applications. Hence, this paper aims to serve as a cheat sheet for those probability distributions of orientations. Models for 1-DOF, 2-DOF and 3-DOF orientations are discussed. For each of them, expressions for the density function, fitting to data, and sampling are presented. The paper is written with a compromise between engineering and statistics in terms of notation and terminology. A Python library with functions for some of these models is provided. Using this library, two examples of applications to real data are presented.

Recent advancements in large language models have revolutionized information access, as these models harness data available on the web to address complex queries, becoming the preferred information source for many users. In certain cases, queries are about publicly available data, which can be effectively answered with data visualizations. In this paper, we investigate the ability of large language models to provide accurate data and relevant visualizations in response to such queries. Specifically, we investigate the ability of GPT-3 and GPT-4 to generate visualizations with dataless prompts, where no data accompanies the query. We evaluate the results of the models by comparing them to visualization cheat sheets created by visualization experts.

Here's the situation you've probably been in already: you join a new project and soon notice the page load speed is… underwhelming. You might assume it's due to your slow network, but after running a Lighthouse report, you find the score is 20 out of 100. Something is definitely wrong here! When I see something like this, I usually have a real urge to fix it. Besides, sometimes it's a nice way to get acquainted with the project as you inevitably go through a lot of code when doing it. Now, what's important is that performance optimizations are a real rabbit hole, and there's almost always something else that can be optimized. The Pareto principle applies here perfectly: a set of things can be done relatively easily, but they can have a meaningful impact.

ChatGPT for Data Science Cheat Sheet by KDnuggets 4 Ways to Generate Passive Income Using ChatGPT by Youssef Rafaat 5 Free Tools For Detecting ChatGPT, GPT3, and GPT2 by Abid Ali Awan 4 Ways to Rename Pandas Columns by Abid Ali Awan Simpson's Paradox and its Implications in Data Science by Nisha Arya GPT-4: Everything You Need To Know by Nisha Arya The ChatGPT Cheat Sheet by KDnuggets 5 SQL Visualization Tools for Data Engineers by Ndz Anthony ChatGPT vs Google Bard: A Comparison of the Technical Differences by Nate Rosidi How to Select Rows and Columns in Pandas Using [ ], .loc,

Data science is a rapidly growing field that combines statistics, mathematics, and computer science to extract insights and knowledge from data. As a data scientist, you need to be proficient in a variety of tools, techniques, and concepts to effectively analyze and visualize data. To help streamline your work, we have created the ultimate data science cheat sheet. The cheat sheet covers all the essential topics in data science, from the basics of statistics and probability to advanced machine learning algorithms and deep learning techniques. It is designed to be a quick reference guide for data scientists, providing a comprehensive overview of the key concepts and tools used in the field.

You've played around with it and have seen the potential power it possesses. You may have even found a few tips or tricks in your travels. But if you want a comprehensive reference for engineering useful prompts for a variety of different tasks, this cheat sheet is where you need to be looking. This fantastic overview was put together by Ricky Costa, an engineer at Neural Magic. Ricky has a history of putting together other great NLP projects, and has lived up to his previous outings this time around. This cheat sheet illustrates the diverse abilities of OpenAI's ChatGPT for developers and content creators to enhance their proficiency in large language model prompting across various domains including media content creation, natural language processing, and programming.

The Data Engineering with GCP is a complete data life cycle cheat sheet for experienced individuals who want to review the essential concepts of the data engineering ecosystem and tools. PySpark Cheat Sheet includes handy commands for handling DataFrames in Python with examples. The cheat covers the basic working of Apache Spark DataFrames from initializing the SparkSession to running queries and saving the data. The dbt(data built tool) commands cheat sheet provides simple examples of various commands that you can use to transform the data. Apache Kafka is a command-based cheat sheet that covers the essential commands for distributed data streaming.

In this post, you will learn about machine learning and deep learning algorithms and frameworks. Furthermore, you will learn tips and tricks on how to handle the data, select metrics, and improve the model performance. The last and most essential cheat sheet is about machine learning interview questions and answers with visual examples. The Machine Learning Algorithms cheat sheet is all about algorithm's description, applications, advantages, and disadvantages. It is your gateway into the world of supervisor and unsupervised machine learning models, where you will learn about linear and tree-based models, clustering, and association.

Today, We'll look after something very big that you might have never seen or rarely seen on the web. We have researched for more than 35 days to find out all the cheatsheets on machine learning, deep learning, data mining, neural networks, big data, artificial intelligence, python, tensorflow, scikit-learn, etc from all over the web. You can also download the pdf version of this cheat sheets (links are already provided below every image). Note: The list is long. So, If you are in hurry, Please check out all the cheat sheets directly on Table of Contents.