James Briggs Courses

How to Use OpenAI Whisper to Fix YouTube Search

Learn to enhance YouTube search using OpenAI Whisper, transformers, and vector search. Build a system for precise content retrieval, transcription, and question-answering from YouTube videos.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

GUI-Based Few Shot Classification Model Trainer - Demo

Discover how to efficiently train classification models using vector search, enabling rapid fine-tuning with minimal labeled data for improved accuracy and targeted results.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

AlexNet and ImageNet Explained

Explore the revolutionary AlexNet CNN and ImageNet dataset, their impact on deep learning, and practical implementation using PyTorch for image classification tasks.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

How to Use Color Histograms for Image Retrieval

Learn to use color histograms for image retrieval, including building histograms, using OpenCV, and implementing retrieval techniques. Explore pros, cons, and practical applications of this content-based approach.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Evaluation Measures for Search and Recommender Systems

Learn popular offline metrics for evaluating search and recommender systems, including Recall@K, MRR, MAP@K, and NDCG@K, with Python demonstrations and practical insights for improving information retrieval systems.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Long Form Question Answering in Haystack

Explore long-form question answering using Haystack, covering setup, data indexing, and generating responses. Learn to implement QA systems for efficient information retrieval in organizations.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Spotify's Podcast Search Explained

Explore Spotify's innovative podcast search system using natural language processing and learn to implement a similar solution with transformer models and vector search techniques.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

Is GPL the Future of Sentence Transformers - Generative Pseudo-Labeling Deep Dive

Explore Generative Pseudo-Labeling for training high-performance sentence transformers using unlabeled text data. Learn implementation techniques, potential applications, and future implications for NLP and semantic search.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Train Sentence Transformers by Generating Queries - GenQ

Learn to train sentence transformers using GenQ, a technique that generates synthetic queries for effective bi-encoder models in semantic search, with practical code walkthroughs and implementation insights.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Streamlit for ML - MUI Card Component Build

Learn to create custom Streamlit components using Material UI design elements, focusing on building an interactive card component for machine learning applications.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

How to Build a Q&A AI in Python - Open-Domain Question-Answering

Learn to build an open-domain question-answering AI in Python, covering retriever training, fine-tuning, evaluation, vector database setup, and querying techniques for natural language interfaces.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Building Transformer Tokenizers - Dhivehi NLP #1

Learn to build an effective WordPiece tokenizer for Dhivehi, a low-resource language, overcoming unique challenges in NLP application. Explore tokenizer components, implementation, and usage for this fascinating language.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Question-Answering in NLP - Extractive QA and Abstractive QA

Explore question-answering in NLP, covering extractive and abstractive QA techniques. Learn about semantic search, ODQA, SQuAD format, and various QA approaches for efficient information retrieval and processing.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

All You Need to Know on Multilingual Sentence Vectors - 1 Model, 50+ Languages

Learn to create and use multilingual sentence vectors for cross-language text comparison. Covers techniques like multi-task training and knowledge distillation, with practical implementation and evaluation steps.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Fine-Tune High Performance Sentence Transformers With Multiple Negatives Ranking

Learn to fine-tune sentence transformers using multiple negatives ranking loss, improving embedding quality for similarity prediction and outperforming SBERT. Includes PyTorch and sentence-transformers library implementations.

• YouTube
• 37 minutes
• Self-Paced
• Free Video