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#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXHow to Run Multiple LLMs Locally Using Llama-Swap on a Single Server
Tired of starting/stopping different models every time you want to test something? Let Llama-Swap handle that for you.
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A Gentle Introduction to Bayesian Regression
In this article, you will learn: β’ The fundamental difference between traditional regression, which uses single fixed values for its parameters, and Bayesian regression, which models them as probability distributions.
How to Develop Powerful Internal LLM Benchmarks
Learn how to compare LLMs using your own interal benchmark The post How to Develop Powerful Internal LLM Benchmarks appeared first on Towards Data Science.
Functional Programming in Python: Leveraging Lambda Functions and Higher-Order Functions
This article will provide you with a deep understanding of functional programming in Python, focusing on lambda functions and higher-order function concepts through detailed explanations and practical code examples.
Using Googleβs LangExtract and Gemma for Structured Data Extraction
Extracting structured information effectively and accurately from long unstructured text with LangExtract andLLMs The post Using Googleβs LangExtract and Gemma for Structured Data Extraction appeared first on Towards Data Science.
Image editing in Gemini just got a major upgrade
Transform images in amazing new ways with updated native image editing in the Gemini app.
Tips for Building Machine Learning Models That Are Actually Useful
A practical guide to moving beyond proof-of-concepts to production-ready machine learning.
Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi
Learn APE, RoPE, and ALiBi positional embeddings for GPT β intuitions, math, PyTorch code, and experiments on TinyStories The post Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi appeared first on Towards Data Science.
Googleβs URL Context Grounding: Another Nail in RAGβs Coffin?
Googleβs hot streak in AI-related releases continues unabated. Just a few days ago, it released a new tool for Gemini called URL context grounding. URL context grounding can be used stand-alone or combined with Google search grounding to conduct deep dives into internet content. What is URL context grounding? In a nutshell, itβs a way [β¦] The post Googleβs URL Context Grounding: Another Nail in RAGβs Coffin? appeared...
10 Useful NumPy One-Liners for Time Series Analysis
Working with time series data often means wrestling with the same patterns over and over: calculating moving averages, detecting spikes, creating features for forecasting models.
7 Python Built-ins That Seem Like a Joke (Until You Use Them)
Donβt take these Python built-ins lightly before you try them out!
LLM Monitoring and Observability: Hands-on with Langfuse
Learn the fundamentals of LLM monitoring and observability, from tracing to evaluation and setting up a dashboard using Langfuse The post LLM Monitoring and Observability: Hands-on with Langfuse appeared first on Towards Data Science.
Why Your Prompts Donβt Belong in Git
The hidden cost of storing prompts in your source code The post Why Your Prompts Donβt Belong in Git appeared first on Towards Data Science.
How to Benchmark Classical Machine Learning Workloads on Google Cloud
Harnessing CPUs for Practical, Cost-Effective Machine Learning The post How to Benchmark Classical Machine Learning Workloads on Google Cloud appeared first on Towards Data Science.
Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers
An Open Letter to the Scientific Community The post Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers appeared first on Towards Data Science.
Learn Python (+ AI) and Become a Certified Data Analyst for FREE This Week
From August 25β31, DataCampβs entire Python and AI curriculum is 100% free. No credit card required. Just unlimited learning.
Systematic LLM Prompt Engineering Using DSPy Optimization
This article is a journey into the fascinating and rapidly evolving science of LLM prompt iteration, which is a fundamental part of Large Language Model Operations (LLMOPs). Weβll use the example of generating customer service responses with a real-world dataset to show how both generator and LLM-judge prompts can be developed in a systematic fashion [β¦] The post Systematic LLM Prompt Engineering Using DSPy...
The Best Way of Running GPT-OSS Locally
The most optimized way to run the GPT-OSS 20B model on RTX 3090 with llama.cpp and Open WebUI Python servers.
Logistic vs SVM vs Random Forest: Which One Wins for Small Datasets?
When you have a small dataset, choosing the right machine learning model can make a big difference.
7 Steps to Mastering No Code Automation with n8n for Data Professionals
Transform your data workflows with this comprehensive guide to building enterprise-grade automation pipelines using n8n's visual workflow platform.
5 Scikit-learn Pipeline Tricks to Supercharge Your Workflow
Perhaps one of the most underrated yet powerful features that scikit-learn has to offer, pipelines are a great ally for building effective and modular machine learning workflows.
Is Googleβs Reveal of Geminiβs Impact Progress or Greenwashing?
On the surface, Google's numbers sound reassuringly small, but the more closely you look, the more complicated the story becomes. The post Is Googleβs Reveal of Geminiβs Impact Progress or Greenwashing? appeared first on Towards Data Science.
Three Essential Hyperparameter Tuning Techniques for Better Machine LearningΒ Models
Learn how to optimize your ML models for betterresults The post Three Essential Hyperparameter Tuning Techniques for Better Machine LearningModels appeared first on Towards Data Science.
Cracking the Density Code: Why MAF Flows Where KDEΒ Stalls
Learn why autoregressive flows are the superior density estimation tool for high-dimensional data The post Cracking the Density Code: Why MAF Flows Where KDEStalls appeared first on Towards Data Science.
Predictive Analytics in Healthcare: Improving Patient Outcomes
Predictive analytics in healthcare is revolutionizing patient care by using AI and machine learning to forecast health outcomes and optimize treatment plans.
Shortcuts for the Long Run: Automated Workflows for Aspiring Data Engineers
Tired of repeating the same data tasks? Automate them. This article shows beginners how to build efficient, low-maintenance data engineering workflows that pay off in the long run.
From JSON to Dashboard: Visualizing DuckDB Queries in Streamlit with Plotly
Learn how to connect several essential tools to develop a simple yet intuitive dashboard.
How to Perform Comprehensive Large Scale LLM Validation
Learn how to validate large scale LLM applications The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science.
What If I Had AI in 2020: Rent The Runway Dynamic Pricing Model
Ever wondered how different things might have been if ChatGPT had existed at the start of Covid? Especially for data scientists who had to update their forecast models? The post What If I Had AI in 2020: Rent The Runway Dynamic Pricing Model appeared first on Towards Data Science.
Where Hurricanes Hit Hardest: A County-Level Analysis with Python
Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years. The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science.
Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning
Accuracy alone doesnβt guarantee trustworthiness. Monotonicity ensures predictions align with common sense and business rules. The post Designing Trustworthy ML Models: Alan & Aida Discover Monotonicity in Machine Learning appeared first on Towards Data Science.
How We Reduced LLM Costs by 90% with 5 Lines ofΒ Code
When clean code hides inefficiencies: what we learned from fixing a few lines of code and saving 90% in LLM cost. The post How We Reduced LLM Costs by 90% with 5 Lines ofCode appeared first on Towards Data Science.
10 Python One-Liners to Optimize Your Machine Learning Pipelines
This tutorial will focus on ten practical one-liners that leverage the power of libraries like Scikit-learn and Pandas to help streamline your machine learning workflows.
How to Build a Lightweight Data Pipeline with Airtable and Python
This article shows how to build a simple, ETL-like pipeline using the Airtable Python API, sticking to Airtable free tier.
Seeing Images Through the Eyes of Decision Trees
In this article, you'll learn to: β’ Turn unstructured, raw image data into structured, informative features.