Natural Language Processing
Explore natural language processing (NLP) concepts, review Text preprocessing and data cleaning, learn forward propagation and back propagation in neural networks, Word Embedding using Keras and deep learning with examples
Modern Techniques for Better Search Relevance in Solr
Explore modern search relevance techniques beyond BM25, including machine learning and NLP. Learn to measure, fine-tune, and implement advanced methods like learning to rank and query intent classification in Apache Solr.
Vector Semantics and Word Embeddings in Data Science
Dive into the mathematical foundations of vector semantics and word embeddings, exploring how words are represented as numerical vectors for natural language processing applications.
Vector Semantics and Word Embeddings in Natural Language Processing
Dive into vector semantics and word embeddings, exploring representation learning, distributional hypothesis, word2vec implementation, and their practical applications in natural language processing.
Word2Vec- Distributed Representations of Words and Phrases and Their Compositionality
Explore Word2Vec, a groundbreaking technique for creating word vectors. Learn about distributed representations, skip-gram model, negative sampling, and subsampling methods to improve vector quality and training efficiency.
Deep Learning for Natural Language Processing
Explore NLP architectures, transformers, language models, and advanced techniques for text generation and embedding learning in this comprehensive deep learning lecture.
Complete Natural Language Processing Machine Learning Tutorial
Comprehensive NLP tutorial covering tokenization, preprocessing, encoding, and word embeddings. Practical implementations and in-depth explanations for beginners and intermediate learners.
Making Sense of Unstructured Threat Data
Explore how Natural Language Processing and Word2Vec can organize unstructured cybersecurity intelligence, making it actionable by mapping to frameworks like MITRE ATT&CK.
Using Text Embedding Algorithms in Recommender Systems
Explore text embedding algorithms' surprising effectiveness in recommendation systems, outperforming state-of-the-art models while offering efficiency for large-scale applications.
Bias in NLP
Exploring ethical implications of word embedding models in NLP, focusing on gender bias in occupational representations derived from Wikipedia data using word2vec algorithm.
Un Vector por Tu Palabra
Exploración de representación distribuida de palabras y su aplicación en clasificación de textos de redes sociales, utilizando el ecosistema científico de Python para experimentos replicables.
Word Embeddings from Deep Space Nine using Machine Learning
Explore word embedding techniques using Star Trek Deep Space Nine scripts, from Bag of Words to BERT, with practical insights for production environments.
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.
How Far is the Empire State Building from the Eiffel Tower? Semantic Vectors in Travel Discovery - Haystack US 2023
Explore semantic vector applications in travel marketplaces for improved recommendations, ranking, and ad optimization. Learn about evolving algorithms and practical advice for implementation.
Deep Learning in NLP and Beyond - 2015
Explore advanced machine learning in NLP, focusing on recurrent neural networks and deep learning. Gain insights into future research directions for developing language-understanding machines.