Towards Data Science @TDataScience
A Medium publication sharing concepts, ideas, and codes. Share your insights and projects with our global audience: https://t.co/Mh1ZLme1o4. towardsdatascience.com We're Global 🌏 Joined October 2016-
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Even quite complicated Python list comprehensions can be more readable than the corresponding for loops - by @Marcin__Kozak buff.ly/3JloJep
"This niche solution will allow you to read gridded binary data files with GDAT file endings produced from computer models, particularly those modeling natural processes, such as environmental or meteorological phenomena." by Madison Hunter buff.ly/3Q3KeDZ
"In this post, you’ll get some guidelines that can help you to create beautiful slide decks. In the end, you can apply the tips to your own project." Combining Storytelling and Design for Unforgettable Presentations by Hennie de Harder buff.ly/3U7b1k4
In this article, Oren Matar explains and demonstrates the concept of structured generative AI: generative AI constrained to defined formats: buff.ly/442xLq5
In this article, Nikita Kiselov shows how to build custom LLM evaluators for specific real-world needs: buff.ly/4aCirmH
In a new, step-by-step tutorial, @vojaydev shows how you can leverage Gemini, Python, FastAPI, Pydantic, and RAG to create an AI-driven movie quiz. buff.ly/3U6BAFZ
"With Neural Speed, which relies on Intel’s extension for Transformers, Intel further accelerates inference for 4-bit LLMs on CPUs." @bnjmn_marie explores a recently released library for efficient LLM inference and tests its promised speed benefits. buff.ly/4b17jQ4
How to Use Python Built-In Decoration to Improve Performance Significantly - How to implement a caching mechanism in Python, and when not to use it? by @qiuyujx buff.ly/3Jh2GWb
An open source database for eclipse chasers by @rohitpandey576 buff.ly/3TXbrto
An Exploration of Model-State Data in Anomaly Detection - The Untapped Potential of Neuron-State Data by Sara Nóbrega buff.ly/3VW72JK
"This article will briefly discuss the current methodologies available that can add constraints to the output of neural networks, with some personal insights included." How to Encode Constraints to the Output of Neural Networks by Runzhong Wang buff.ly/3U19ewW
6 Steps to Make AI Write Your Python Code for You Use the INSPIRe framework to save time and gain a competitive edge (ChatGPT-4 — Claude 3 — Gemini) by @Nabil_Alouani_ buff.ly/4awju7x
Advanced Code Generation With LLMs — Building a Synthetic Data Generator Applying the 6 steps of the INSPIRe framework to accelerate your code generation (ChatGPT-4 — Claude 3 — Gemini) by @Nabil_Alouani_ buff.ly/43XRIyc
Building a RAG chain using LangChain Expression Language (LCEL) - Learning the building blocks of LCEL to develop increasingly complex RAG chains by @RSK2327 buff.ly/4cVRwDB
If you need to train a specialized LLM and are looking for a resource-conscious approach, @cwolferesearch walks us through several options in his latest deep dive, paying special attention to Low-Rank Adaptation (LoRA). buff.ly/49Hfell
"For people who are interested in transitioning to a data science or AI role, find any way to complete a successful data science project." Based on his experience prior to joining LinkedIn, @JimmyWongUSA stresses the importance of hands-on projects. buff.ly/49CNSfY
What's the best way to help your non-technical colleagues learn some SQL for their daily workflows? Jordan Gomes shares insights based on his own experience running internal trainings. buff.ly/3T9NoXz
Follow along Rhys Goldstein's fascinating new deep dive to discover five 3D grid neighborhoods you can use to solve AI problems like navigation and visibility in 3D. buff.ly/4a6xiVw
Gender bias has been a perennial issue in AI systems and LLM outputs. @ArtFishAI provides a thorough overview of research into this issue, and discusses "what it means to 'fix' bias." buff.ly/3v2qdGI
How well do LLMs perform when conducting numeric evaluations? @aparnadhinak shares findings from her recent experimentations and unpacks some nontrivial issues and limitations to keep in mind. buff.ly/3IpzJa9
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Andrew Ng @AndrewYNg
1.0M Followers 908 Following Co-Founder of Coursera; Stanford CS adjunct faculty. Former head of Baidu AI Group/Google Brain. #ai #machinelearning, #deeplearning #MOOCs
elvis @omarsar0
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"As AI technology progresses such is the expectation to adopt it and build smarter AI-driven products and we as AI-experts hold the responsibility in supporting it across the organization." My latest post on @TDataScience towardsdatascience.com/the-role-of-da…
Excited to have my article on #LLM deployment shared with Towards Data Science. Check it out 👇🏻
For a hands-on introduction to quantization and LLM deployment, don't miss Ayoola Olafenwa's concise step-by-step tutorial. buff.ly/3U3vyGo
@TDataScience This tutorial by Ayoola Olafenwa is a gem for those diving into quantization and LLM deployment - practical and insightful!
“Addressing Spatial Dependencies” by Leonardo Maldonado, published on @TDataScience #spatialanalysis #statistics #economics #datascience #remotesensing towardsdatascience.com/addressing-spa…
Seems pretty useful: A new correlation coefficient for non-monotonic and non-linear relationships (e.g., cubic, quadratic), available in R and Python package xicor towardsdatascience.com/a-new-coeffici…
💻⌛️My latest tech post in Towards Data Science. Discover seven requisite skills for navigating from data science projects to production-ready applications.
What should entry-level data scientists focus on to transform their theoretical knowledge into industry-level applications? @San_Onion_Young outlines seven essential skills. buff.ly/4cUPgN2
📝Thrilled to share that my latest blog post, “Addressing Spatial Dependencies - The Essentials of the Spatial Error Model (SEM),” has found a home on @TDataScience! Thank you to the editors for embracing my work and allowing me to be a contributing writer. #DataScience…
Addressing Spatial Dependencies - The Essentials of the Spatial Error Model (SEM) by @ljmaldon buff.ly/3TZFyAl
Check out our InCTRL, a generalist model for image anomaly detection, to be published at #CVPR2024 🔥🔥🔥
"Generalist Anomaly Detection (GAD) aims to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data." by @Guansong_Aus buff.ly/3UfRf7n
Really enjoy those kind of articles... 😆 A New Coefficient of Correlation by Tim Sumner in @TDataScience towardsdatascience.com/a-new-coeffici…
We continue exploring foundation time series forecasting models at @ZAAI_zero_to_AI. You can check our latest article in @TDataScience 🚀
The world of time series foundation models continues to evolve at a rapid pace. @luisbrasroque and Rafael Guedes explore one of the latest additions, Salesforce's Moirai model, and compare its performance to that of TimeGPT and Chronos. buff.ly/4cSLWly
Our customers are using Relari's "simulated user feedback" to rapidly iterate on LLM products. Take a look at how it works!
In their latest research, @AntonanteP and @Yiszzzz present a novel approach for using production data to simulate user feedback, which you can leverage for testing and evaluating your LLM app. buff.ly/4awRUH8
Accelerate Pandas up to 100x by leveraging GPUs. Naser Timimi just published a great introduction to RAPIDS cuDF complete with examples to get started quickly. Read it here. bit.ly/3JwsFZX
Another amazing blog post from @mmbronstein 🥳 "The Road to Biology 2.0 Will Pass Through Black-Box Data" Particularly uplifting for biologists and budding Geometric DL enthusiasts -- like myself :) -- eager to explore representation learning in science! medium.com/towards-data-s…
A little 'dirty secret' of Scikit-learn is that 'predict_proba' does not actually predict probabilities and the function name is a complete misnomer. Want to know why? This great article explains why #calibration #machinelearning towardsdatascience.com/pythons-predic…
The rise of foundation models for time series forecasting 🏔️ @ZAAI_zero_to_AI
In their latest collaboration, @luisbrasroque and Rafael Guedes provide an in-depth explanation of the architecture behind Chronos, Amazon's foundation model for time series forecasting, and compare its performance to TiDE's (Time-series Dense Encoder). buff.ly/3xpxukQ
Definitely one of the best and succinct blog posts I've read summarizing the key optimization steps for @ApacheSpark jobs that any data engineer (and scientist) should be familiar with. Well written, great graphics. towardsdatascience.com/6-recommendati…
A Proof of the Central Limit Theorem by @TheStatsStory in @TDataScience buff.ly/3VS4kFj
In RAG, generation is just as important as retrieval:
When evaluating the performance of RAG pipelines, @aparnadhinak recommends that we focus not just on retrieval, but also pay enough attention to the generation part. buff.ly/4aKoyVq
In my most recent article for @TDataScience, I explain why ANOVAs and T-test are not suitable for the analysis of categorical variables (a widespread practice in biomedicine). #Rstats #PhDlife #PhD @PhDVoice
In this article, @danielneuroglia looks at a viable alternative for analyzing ordered categorical variables using R, brms and elements from the tidyverse. Avoiding abuse and misuse of T-test and ANOVA: Regression for categorical responses buff.ly/3TN9D64
@TDataScience Great article! Clear and detailed at the same time!
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