James Briggs Courses

How to Build a Bert WordPiece Tokenizer in Python and HuggingFace

Learn to build a custom BERT WordPiece tokenizer in Python using HuggingFace, essential for creating transformer models for specific languages or domains. Includes step-by-step walkthrough and code implementation.

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

Product Quantization for Vector Similarity Search - Python

Learn to compress high-dimensional vectors using product quantization, reducing memory usage by 97% and improving search speeds by 92x. Includes Python implementation and visualization techniques.

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

Choosing Indexes for Similarity Search - Faiss in Python

Explore Faiss indexes for efficient similarity search, comparing Flat, LSH, HNSW, and IVF to optimize performance in large-scale datasets. Learn to choose and implement the best index for your needs.

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

FAISS - Introduction to Similarity Search

Explore efficient similarity search with Facebook AI Similarity Search (FAISS). Learn implementation, options, and how FAISS enhances semantic search speed.

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

Training With BertForPretraining

Explore BERT training techniques, focusing on NSP and MLM logic, with practical implementation steps and code examples for effective model fine-tuning.

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

Training BERT - Train With Masked-Language Modeling

Learn to fine-tune BERT models using masked language modeling in PyTorch for domain-specific NLP tasks, enhancing performance beyond out-of-the-box capabilities.

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

Sentence Similarity With Transformers and PyTorch

Learn to measure semantic similarity between sentences using BERT and PyTorch. Explore tokenization, vector creation, and cosine similarity calculation for powerful NLP applications.

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

Multi-Class Language Classification With BERT in TensorFlow

Learn to build a multi-class language classification model using BERT and TensorFlow. Covers data preprocessing, model architecture, training, and prediction with transformers for NLP tasks.

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

Identify Stocks on Reddit with SpaCy - NER in Python

Learn to extract stock mentions from Reddit using SpaCy's Named Entity Recognition, enabling automated classification and analysis of unstructured text data for financial insights.

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

How to Use the Reddit API in Python

Learn to set up, authorize, and extract data from Reddit using Python, covering API basics, data retrieval techniques, and advanced features for comprehensive subreddit analysis.

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

SPLADE - The First Search Model to Beat BM25

Explore SPLADE, a groundbreaking sparse embedding model for AI-powered search. Learn its advantages over traditional methods, implementation techniques, and potential future developments in vector search technology.

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

Convolutional Neural Nets Explained and Implemented in Python - PyTorch

Comprehensive guide to Convolutional Neural Networks: theory, implementation, and practical applications using Python and PyTorch for computer vision tasks like image classification.

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

Advanced Sentiment Analysis with NLP Transformers and Vector Search

Learn advanced sentiment analysis techniques using NLP transformers and vector search. Apply these methods to large datasets, generate insights, and create queryable databases for understanding customer perceptions in the hotel industry.

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

Vision Transformers Explained + Fine-Tuning in Python

Explore Vision Transformers (ViT) for image classification, from theory to implementation. Learn about attention mechanisms, patch embeddings, and fine-tuning using Hugging Face in Python.

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

Fast Zero Shot Object Detection with OpenAI CLIP

Learn zero-shot object detection and localization using OpenAI CLIP, a multi-modal deep learning model. Implement practical techniques for efficient, domain-flexible computer vision tasks without fine-tuning.

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