Learn how to effectively combine Pandas, NumPy, and scikit-learn in a unified workflow to build powerful machine learning solutions from raw data to accurate predictions.
Creating a Qwen-Powered Lightweight Personal Assistant
This article shows you how to set up and run a personal assistant application in Python powered by a Qwen model.
Attention May Be All We Need… But Why?
This article takes a closer look at the attention mechanism of transformer architectures.
Creating a Secure Machine Learning API with FastAPI and Docker
Learn how to turn your machine learning model into a safe and scalable API using FastAPI and Docker.
Zero-Shot and Few-Shot Learning with Reasoning LLMs
This article briefly introduces reasoning LLMs and analyzes two common learning approaches they use to address complex tasks: zero-shot learning and few-shot learning.
Automate Dataset Labeling with Active Learning
Why label thousands of data points when your model can tell you exactly which ones it needs?
7 Emerging Trends in Generative AI and Their Real-World Impact
This article explores seven prominent trends and how they are already transforming the world around us.
5 Problems Encountered Fine-Tuning LLMs with Solutions
What could go wrong and how to successfully navigate it when fine-tuning LLMs.
PyTorch Lightning Hyperparameter Optimization with Optuna
This article shows how to jointly use PyTorch Lightning and Optuna to guide the hyperparameter optimization process for a deep learning model.
Advanced Techniques to Build Your RAG System
In the previous post, you learned how to build a simple retrieval-augmented generation (RAG) system. RAG is a powerful approach for enhancing large language models with external knowledge and there are many variations in how to make it work better. In the following, you will see some advanced features and techniques to improve the performance […]