Build a full client-side toxicity detection solution using Transformers.js, ONNX Runtime, and the `all-MiniLM-L6-v2` model. Train a custom model on the Kaggle Toxic Comment Classification Challenge dataset, convert it to ONNX, and run it in the browser for fast, private text analysis. Get 98% accuracy with this approach, see the code, and try the live demo.
Learn how to build and deploy a custom sentiment analysis model for the web using PyTorch and Google's LiteRT! This guide walks you through the process of creating a model from scratch, training it on a YouTube comments dataset, converting it to a browser-friendly format, and running it in the browser with TensorFlow Lite and the Google Gen AI JavaScript library. Perfect for web developers looking to leverage custom AI models.
Understand how temperature affects Large Language Model (LLM) output. Learn how temperature parameter changes probability distribution of next-token predictions, impacting creativity and predictability. Explore a visualization tool demonstrating the effects of temperature and top-k parameters on LLM responses.
Learn Python for AI development: This hands-on guide details a practical approach to mastering Python, focusing on effective learning techniques and addressing the impact of AI-powered code completion tools on the learning process. Discover how to balance AI assistance with focused practice for optimal skill acquisition in Python programming for AI projects.
Troubleshoot creating composite indexes with vector embeddings in Firestore on Windows. This solution uses a JSON file to define the index, bypassing Powershell JSON escaping issues, and provides the corrected `gcloud` command for successful index creation. Learn how to create a functional composite index with vector embeddings.