Hierarchical Clustering: A Tree-Based Approach to Data Grouping

Hierarchical Clustering: A Tree-Based Approach to Data Grouping

⋅

In this blog, you will explore hierarchical clustering in Python, understand its application in machine learning, and review a practical hierarchical clustering example. We will delve into the hierarchical clustering algorithm, compare its implementation in R, and discuss its significance in data mining. What is Hierarchical Clustering?Types of Hierarchical ClusteringAgglomerative Clustering HierarchicalDivisive Hierarchical ClusteringSupervised vs Unsupervised LearningWhy Hierarchical Clustering?No Need to Pre-specify Number of ClustersCaptures Nested ClustersFlexibility with Cluster ShapesDistance Metrics and Linkage CriteriaHandling OutliersRobustness to InitializationVisual InterpretationPractical ExampleSteps […]

Ver mais

Liked Liked

RMNIST with annealing and ensembling

⋅

By Michael Nielsen In the last post I described Reduced MNIST, or RMNIST, a very stripped-down version of the MNIST training set. As a side project, I’ve been exploring RMNIST as an entree to the problem of using machines to generalize from extremely small data sets, as humans often do. Using just 10 examples of each training digit, in that post I described how to achieve a classification accuracy of 92.07%. That 92.07% accuracy was achieved using a […]

Ver mais

Liked Liked
Google announces big Gemini-powered Translate update you’ve been waiting for

Google announces big Gemini-powered Translate update you’ve been waiting for

⋅

If you ever wanted a Google Translate update, the company is finally making your wish come true. The company today announced what is probably the biggest Translate upgrade in years. As part of this update, Google is integrating Gemini right into the heart of Google Translate across Search and the mobile app. What’s interesting is that Google is promising translations that understand what you meant, not just the words you typed. Google announces natural, accurate text translation in […]

Ver mais

Liked Liked

From Gemma 3 270M to FunctionGemma, How Google AI Built a Compact Function Calling Specialist for Edge Workloads

⋅

Google has released FunctionGemma, a specialized version of the Gemma 3 270M model that is trained specifically for function calling and designed to run as an edge agent that maps natural language to executable API actions. But, What is FunctionGemma? FunctionGemma is a 270M parameter text only transformer based on Gemma 3 270M. It keeps the same architecture as Gemma 3 and is released as an open model under the Gemma license, but the training objective and chat […]

Ver mais

Liked Liked
LLM API Token Caching: The 90% Cost Reduction Feature when building AI Applications

LLM API Token Caching: The 90% Cost Reduction Feature when building AI Applications

⋅

Author(s): Nikhil Originally published on Towards AI. LLM API Token Caching: The 90% Cost Reduction Feature when building AI Applications If you’ve used Claude, GPT-4, or any modern LLM API, you’ve been spending far more than necessary on token processing if you are not caching the system prompt or any prompt that just static and doesn’t change for every api call. Cost comparison: 10x savings on cached token readsToken caching provides substantial cost benefits by allowing reuse of […]

Ver mais

Liked Liked
A Researcher’s Guide to LLM Grounding

A Researcher’s Guide to LLM Grounding

⋅

TL;DR Grounding augments the pre-trained knowledge of Large Language Models (LLMs) by providing relevant external knowledge along with the task prompt. Retrieval-augmented generation (RAG), which builds decades-long work in information retrieval, is the leading grounding strategy. The key challenges in LLM grounding revolve around data. It has to be relevant to the task, available in the right quantity, and prepared in the right format. When providing information to an LLM, less is more. Research shows that it is […]

Ver mais

Liked Liked

Load More

↑

Digitado © 2025