Build, deploy & scale AI Agents with a single button!
xpander is your plug-and-play Backend for agents that manages memory, tools, multi-user states, events, guardrails, and more.
Works with LlamaIndex, Langchain, CrewAI, Google ADK—you name it.
GitHub repo → (don’t forget to star it)
Thanks to Xpander for partnering today!
An Animated Guide to KMeans
If you have ever struggled to understand the KMeans clustering algorithm, such as:
How are the data points assigned to centroids?
How are the centroids reassigned?
When does the algorithm coverage, and more?
…then we created the above video using Manim to help you build an intuitive understanding.
It covers all the steps that we typically follow in KMeans.
Do note that the centroid initialization step in the video is based on randomly selecting k
centroids. But this can vary based on your implementation.
👉 Over to you: Let us know if you wish to see more such animations of ML algorithms.
Thanks for reading!
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn how to build Agentic systems in an ongoing crash course with 13 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
Share this post