Fine-Tuning AI Models: From GPT-3.5 to Custom Domain Expert
Fine-tuned GPT-3.5 for legal document analysis - 70% → 95% accuracy. Complete guide with code, data prep, and evaluation
🏷️ machine-learning
13 posts
AI-Powered Recommendation System: From 5% to 35% CTR
Built hybrid recommendation system using collaborative filtering + LLMs. Increased CTR from 5% to 35% and revenue by $2M/year
Meta's LLaMA - Open Source AI Revolution
Exploring Meta's LLaMA open-source language model, including local deployment, fine-tuning, and comparison with proprietary models.
Fine-Tuning GPT-3.5 for Custom Use Cases
Step-by-step guide to fine-tuning GPT-3.5 for domain-specific tasks, including data preparation, training, and evaluation.
Running Stable Diffusion Locally - Complete Setup Guide
A comprehensive guide to setting up and running Stable Diffusion on your local machine, including hardware requirements, installation steps, and optimization tips.
OpenAI Codex - The AI Behind GitHub Copilot
Exploring OpenAI Codex capabilities for code generation, translation, and explanation across multiple programming languages.
DALL-E Image Generation - Exploring OpenAI's Text-to-Image Model
Experimenting with DALL-E for text-to-image generation, exploring its capabilities, limitations, and potential applications.
Building a Sentiment Analysis System with Transformers
Built sentiment analysis with BERT - 94% accuracy on customer reviews. Processed 100K reviews/day, identified issues 3 days earlier
Building a Recommendation System: From Collaborative Filtering to Deep Learning
Built recommendation engine - increased engagement 35%, revenue +25%, personalized recommendations for 1M users
Deploying Machine Learning Models to Production with FastAPI
Deployed ML model to production - 1000 predictions/s, <100ms latency, auto-scaling. Serving 1M predictions/day
Time Series Forecasting with LSTM: Predicting Server Load
Built LSTM forecasting model - 85% accuracy, predicts server load 24h ahead, auto-scaling saves $5K/month
Named Entity Recognition with spaCy and BERT
Built NER system for document processing - 92% accuracy, extracts entities from 10K docs/hour, automated data extraction
Text Classification with Transformers: From 80% to 95% Accuracy
Upgraded from traditional ML to BERT transformers - accuracy 80% → 95%, handles context better, production-ready