Understanding RAG Part VI: Effective Retrieval Optimization
Be sure to check out the previous articles in this series: •
Understanding Probability Distributions for Machine Learning with Python
In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and data, applying optimization processes with stochastic settings, and performing inference processes, to name a few.
How to Do Named Entity Recognition (NER) with a BERT Model
This post is in six parts; they are: • The Complexity of NER Systems • The Evolution of NER Technology • BERT’s Revolutionary Approach to NER • Using DistilBERT with Hugging Face’s Pipeline • Using DistilBERT Explicitly with AutoModelForTokenClassification • Best Practices for NER Implementation The challenge of Named Entity Recognition extends far beyond simple […]
Understanding RAG Part V: Managing Context Length
Be sure to check out the previous articles in this series: •
Integrating TensorFlow and NumPy for Custom Operations
Combining the power of
Prompt Engineering Patterns for Successful RAG Implementations
You know it as well as I do: people are relying more and more on generative AI and large language models (LLM) for quick and easy information acquisition.
Implementing Multi-Modal RAG Systems
Large language models (LLMs) have evolved and permeated our lives so much and so quickly that many we have become dependent on them in all sorts of scenarios.
Next-Level Data Science (7-Day Mini-Course)
Before we start, let’s ensure you are in the right place.
Creating Powerful Ensemble Models with PyCaret
Machine learning is changing how we solve problems.
Kernel Methods in Machine Learning with Python
Kernel methods are a powerful class of machine learning algorithm that allow us to perform complex, non-linear transformations of data without explicitly computing the transformed feature space.
