James Briggs Courses

Today Unsupervised Sentence Transformers, Tomorrow Skynet - How TSDAE Works

Explore unsupervised fine-tuning for sentence transformers using TSDAE. Learn to train models without labeled data, ideal for domains and languages with limited resources.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Intro to Sentence Embeddings with Transformers

Learn to create and apply sentence embeddings using transformers for semantic similarity tasks, exploring models like BERT and GPT for advanced natural language processing applications.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Intro to Dense Vectors for NLP and Vision

Explore dense vector representations in NLP and computer vision, from word2vec to modern transformer models, with practical Python implementations and applications.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Best Indexes for Similarity Search in Faiss

Explore composite indexes for optimizing vector similarity search, focusing on IVFADC, Multi-D-ADC, and IVF-HNSW methods in Faiss to balance recall, latency, and memory usage.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Build NLP Pipelines with HuggingFace Datasets

Explore HuggingFace Datasets for NLP: Learn to access, load, select, and modify datasets, build efficient pre-processing pipelines, and leverage powerful functions for NLP tasks.

• YouTube
• 34 minutes
• On-Demand
• Free Video

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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• Free Video