Learn to Love Working with Vector Embeddings
Unlock the power of machine learning. Our guides will help you conquer vector embeddings and build better applications.
Algorithms & Libraries
BERTopic: The Future of Topic Modeling
Cross-Entropy Loss
Filtering: The Missing WHERE Clause in Vector Search
Introduction to Transfer Learning
Semantic Search: Measuring Meaning From Jaccard to Bert
Softmax Activation Function: Everything You Need to Know
Transformers Are All You Need
Ludicrous BERT Search Speeds
Introduction to K-Means Clustering
K-Nearest Neighbor (KNN) Explained
Evaluation Measures in Information Retrieval
Applications of Vector Search
Embeddings to Identify Fake News
Time Series Analysis Through Vectorization
Language Embedding Models in Financial Services
Faiss: The Missing Manual
Introduction to Facebook AI Similarity Search (Faiss)
Nearest Neighbor Indexes for Similarity Search
Locality Sensitive Hashing (LSH): The Illustrated Guide
Random Projection for Locality Sensitive Hashing
Product Quantization
Hierarchical Navigable Small Worlds (HNSW)
Composite Indexes and the Faiss Index Factory
NLP for Semantic Search
Dense Vectors
Sentence Transformers and Embeddings
Training Sentence Transformers with Softmax Loss
Training Sentence Transformers with MNR Loss
Multilingual Sentence Transformers
Unsupervised Training for Sentence Transformers
Data Augmentation with BERT
Domain Transfer with BERT
The Art of Asking Questions with GenQ
Domain Adaptation with Generative Pseudo-Labeling (GPL)
Projects
Question Answering
Question Answering
Long Form Question Answering in Haystack
Retrievers for Question-Answering
Readers for Question-Answering
Vector Search 101
What is Similarity Search?
What are Vector Embeddings?
What is a Vector Database?
Vector Search for Developers: A Gentle Introduction
Vector Search in the Wild
What will you build?
Upgrade your search or recommendation systems with just a few lines of code, or contact us for help.