Data Elixir - Data Elixir - Issue 443
ISSUE 443 · July 11, 2023Talks & ConferencesGenerative AI with LLMs: Hands-On Training feat.This presentation by Jon Krohn is a great introduction to large language models. It's easy to follow and covers a lot of ground, starting with the technologies that got us to where we are today. The discussion continues with what modern LLMs are capable of, how to train and deploy LLMs, and ideas for getting commercial value from LLMs. Sponsored LinkComplete customer profiles in your data warehouseRudderStack Profiles takes the SQL grunt work out of building customer profiles. You specify the customer traits, then Profiles runs the joins and computations for you to create complete profiles, so you can build better models, faster. And that’s just one use case. Get all the details. Posts & TutorialsHow to Peek at Statistical Tests w/o Breaking ThemContinuously monitoring A/B tests results in a considerable over-estimation of statistical significance. Many tests that should not be considered significant get misinterpreted. Generally, p-values are deeply problematic and will only make you regret your decisions. This post explores the problems of peeking and ways to alleviate them. Joins 13 WaysInner joins are common in the world of databases, but one thing about them is that everyone seems to have a different idea of what they are. In this post, Justin Jaffray explores different definitions of Inner Joins, ways to think about them, and ways to implement them. Some are arguably the same, but they’re all interesting nonetheless. Demystifying Text DataText data isn't structured in neat rows and columns like numerical data and it can be tricky to work with. In this tutorial, Saeed Esmaili walks through a few options, and in particular, shows how to use the python data transformation library called "unstructured." CLI tools hidden in the Python standard libraryTypical Python installations contain a lot of little tools that most people don't know about. This post shows how to find those hidden tools and discover what they do. Webinar: How to generate business intelligence leveraging Yelp's rich first-party data on AWSDiscover how to create actionable insights using Yelp's robust data sets to analyze your marketplace, your customers, and grow your business. Explore use cases on how businesses leverage this rich data with AWS Data Exchange to make strategic business decisions. Register now. Date: July 19, 2023 // sponsored Tools & CodeMakieMakie is an open-source, modern plotting library for Julia. It's a general-purpose tool that's powerful and packed with features that make it easy to explore large datasets, create graphics for publications, build dashboards, create interactive visualizations for the web, and more. Messy DatesMost packages for working with dates in R expect the dates to be in the standard yyyy-mm-dd format. But dates are often messy. {messydates} is an R package that allows for date translations from unstructured text, such as “Second of April, two thousand and twenty”, “26 BC”, as well as inputs that have uncertain, approximate, and sets/ranges of dates. CareerHow to Do Great WorkIf you collected lists of techniques for doing great work in a lot of different fields, what would the intersection look like? How do ambitious people decide what to work on? What do they avoid? What are their common stumbling blocks? How important is luck? Why do nerds have an advantage? Why do rule-breakers rule?... Great post! ResourcesUnraveling Principal Component Analysis (PCA)PCA is a popular technique for analyzing datasets that have lots of dimensions for each data point and is commonly used in machine learning. This new book starts with the basics and then dives deep into the fundamentals, including eigenvectors, the spectral theorem, singular value decompositions, low-rank decompositions and more. For Python! An Introduction to Statistical LearningThe newest edition of this popular statistics text was released a few days ago and this time, it's tailored for Python users. Topics cover some of the most important modeling and prediction techniques and is intended to be accessible to a broad audience. Follow the links to download the PDF. Was this email forwarded to you? Sign up here >> |
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Data Elixir - Issue 442
Tuesday, June 27, 2023
Polars cookbook. LLM-powered autonomous agents. Time series with ML. Scalable & extensible viz. ML system design.
Data Elixir - Issue 441
Tuesday, June 20, 2023
Julia programming for ML. Spatial statistics. Raincloud plots. Artifact corrections for effect sizes. Perils of faking data in Excel. Private LLMs for DB interactions.
Data Elixir - Issue 440
Tuesday, June 13, 2023
NFL Analytics. Sequential testing. Data + Music. Managing generative AI risks. FinGPT: open-source LLM for finance. Data exploration toolkit.
Data Elixir - Issue 439
Monday, June 12, 2023
Data podcasts. What are embeddings? Road trip maps. Dependency management. The {marginaleffects} book. A first course in causal inference.
Data Elixir - Issue 438
Tuesday, May 30, 2023
State of GPT. Interview questions and answers. Hierarchical vs rectangular data. Intro to Vega-Lite.
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