Analytics Vidhya
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For those of you, who are wondering what is “Analytics Vidhya”, “Analytics” can be defined as the science of extracting insights from raw data. The spectrum of analytics starts from capturing data and evolves into using insights / trends from this data to make informed decisions. “Vidhya” on the other hand is a Sanskrit noun meaning “Knowledge” or “Clarity on a subject”. Knowledge, which has been gained through reading literature or through self practice / experimentation.
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals. Source
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| Scope | International, Trade/B2B |
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| Language | English |
| Country | India |
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Recent Articles
Search ArticlesHow to Measure Video Similarity: 6 Techniques I Tested (and the One I Shipped)
Reading list Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on. Instead I watched supposedly-smart methods rank near-identical clips in nonsense orders, and the one that looked best on paper was too slow to actually use.
Handling Imbalanced Classification: What Works Better Than SMOTE
Reading list Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. This guide goes beyond SMOTE.
RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are three of the most widely used tools for measuring RAG quality. In this article, I’ll break down how each one works and when to reach for it.
GPT-5.6 Is Here: Sol, Terra, and Luna
Reading list For twelve days, the best AI models on the planet existed and almost nobody could touch them. That ends now! GPT-5.6 Sol, Terra, and Luna go public today! The models are accessible by all users (no subscription required) This is the full breakdown of what’s on offer: three models, four prices, one precedent, and a capability table that should help you select the right model. Hands-on results follow the moment access opens.
Loop Engineering for AI Agents: How /loop is Changing AI Workflows
AI agents are moving from one-time assistants to persistent workers that can repeat tasks, monitor changes, run checks, update workflows, and return with results. Instead of prompting an LLM once and deciding every next step manually, teams can now use AI agents that keep working (on a Loop) until a goal or stop condition is met. This matters because real work is rarely a single prompt.
DeepSeek DSpark: The Speculative Decoding Trick Behind 400% Faster LLM
DeepSeek’s new DSpark module brings speculative decoding to DeepSeek-V4. It might look like a niche inference tweak, but in production it boosted per-user generation speed by 60 to 85 percent with no drop in model quality. What sets DSpark apart is that it tackles two longstanding problems at once, weak draft quality and the waste of verifying drafts, where prior methods addressed only one. In this article, I’ll break down how it solves both and why that matters at production scale.
OKF: Redefining Knowledge Bases for AI Agents
Reading list In June 2026, Google introduced the Open Knowledge Format (OKF), an open specification for how AI agents organise and exchange knowledge. An OKF bundle is just Markdown files, lightweight YAML metadata, and links between concepts, yet it challenges the assumption that every AI application needs embeddings and vector databases. Because the knowledge base is plain text, it can be version-controlled in Git and navigated by following links rather than retrieving disconnected chunks.
Modern VLMs Explained: How GPT-4o, Gemini, Claude Vision, and Qwen-VL Work
Vision Language Models, or VLMs, are AI models that can understand both visual content and language. While earlier models like CLIP and BLIP connected images with text, modern VLMs can analyze images, read documents, interpret charts, answer visual questions, and support multimodal conversations. Models like GPT-4o, Gemini, Claude Vision, and Qwen-VL are making visual AI more practical for real-world tasks across education, business, healthcare, automation, and accessibility.
YOLO26 Tutorial: Object Detection, Pose Estimation & More
Reading list Looking to model to implement pose estimation? I know something that can perform detection, instance segmentation, pose estimation and classification, all of that in real-time. Yes, I’m talking about the YOLO26 from ultralytics. It can aid security systems or can be fine-tuned to detect even smaller objects. Wondering how to get started? No worries, we’ll cover the basics of YOLO and learn to perform inference using the model.
Large Action Models (LAMs) vs Agentic LLMs: What’s the Real Difference?
Reading list You tell your AI “Polish my email and send it.” A chatbot hands you a paragraph on how that’s done. An agentic LLM opens your inbox and tries. Sometimes it works. Sometimes it clicks the wrong button three times. A Large Action Model just does it, confirms, and moves on. Same sentence, three outcomes. The gap between Large Action Models (LAMs) and agentic LLMs is one of the most practically important distinctions in AI today, and also one of the least clearly explained.