Featured Articles
- Logistic Regression with Jupyter
Learn how to implement logistic regression on the Iris dataset using Jupyter Notebook in this hands-on guide. From data preparation to model deployment, this series walks you through each step with practical code examples and best practices, perfect for data science enthusiasts looking to sharpen their skills.
- Reducing Model Size and Complexity
Unlock powerful combinations of model compression techniques like pruning, quantization, and knowledge distillation to supercharge your neural networks. Discover how these synergistic strategies can slash computational demands, boost efficiency, and keep your models blazing fast and ready for real-world deployment!
- LLMs and Vector Databases
Explore the best practices for integrating large language models (LLMs) and vector databases to optimize performance and efficiency in production settings. This article covers combining model compression techniques, leveraging advanced indexing in vector databases, and implementing contextual filtering to enhance retrieval accuracy and scalability