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Learn to evaluate search and recommender systems using popular offline metrics like Recall@K, MRR, MAP@K, and NDCG@K with Python in less than an hour with James Briggs.
Explore the subdomain of Natural Language Processing (NLP) with a focus on Question-Answering (QA) in less than an hour. Offered by James Briggs, this study delves into QA tools, their benefits, and applications.
Explore Spotify's podcast search system in under an hour with James Briggs. Learn how to implement a similar natural language search system for podcasts.
Explore the potential of Generative Pseudo-Labeling (GPL) in training sentence transformers with unlabeled text data in this short, in-depth study by James Briggs.
Learn to fine-tune bi-encoder models for semantic search using GenQ, a method that generates synthetic training data. Offered by James Briggs, it takes less than an hour.
Learn to create custom components using Streamlit for ML, focusing on an interactive card component with Material UI design elements. Offered by James Briggs, under 1 hour.
Learn to build a Q&A AI using Python in less than an hour with James Briggs. Master open-domain question-answering (ODQA) to retrieve information in a natural, human-like way.
Explore the unique language of Dhivehi and learn to build an effective WordPiece tokenizer in this short, engaging program by James Briggs.
Explore semantic search and question-answering in NLP with James Briggs. Learn about different QA forms, their components, and applications in under an hour.
Learn to develop multilingual sentence transformers with James Briggs in under an hour. Understand how multilingual models work, the datasets to use, and how to apply pretrained models.
Learn to fine-tune high-quality sentence transformers using multiple negatives ranking (MNR) loss in under an hour with James Briggs.
Learn to fine-tune sentence transformers using NLI softmax loss with James Briggs. This under 1-hour material covers training, preprocessing, and results.
James Briggs offers a concise guide to understanding and implementing HNSW for vector similarity search using Faiss in Python, in under an hour.
Learn to apply fast and accurate filters to vector searches on massive datasets with James Briggs' under 1-hour material, including Pinecone's solution to filtering.
Learn the traditional approach to Locality Sensitive Hashing (LSH) with James Briggs. This under 1-hour material covers shingling, MinHashing, and banded LSH function.
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