A tutorial on fine-tuning a German GPT-2 language model for text generation using Huggingface's Transformers library and a dataset of recipes.
A chronological survey of key NLP models and techniques for supervised learning, from early RNNs to modern transformers like BERT and T5.
An analysis of OpenAI's GPT-3 language model, focusing on its 175B parameters, in-context learning capabilities, and performance on NLP tasks.
A summary of a meetup talk on advanced recommender systems, exploring techniques beyond baselines using graph and NLP methods.
Explores improving recommender systems using graph-based methods and NLP techniques like word2vec and DeepWalk in PyTorch.
Analyzing tweet sentiment towards public figures using R, word embeddings, and logistic regression models to measure online negativity.
A developer explores investigative journalism, drawing parallels between source control diffs and uncovering truth in legal documents and online comments.
A tutorial on text data classification using the BBC news dataset and PHP-ML for machine learning, covering data loading and preprocessing.
A technical overview of the evolution of large-scale pre-trained language models like BERT, GPT, and T5, focusing on contextual embeddings and transfer learning in NLP.
Explores word morphing using word2vec embeddings and A* search to find semantic paths between words, like 'tooth' to 'light'.
Part 4 of a series on the Microsoft Bot Framework, focusing on adding natural language processing using LUIS (intents, entities, utterances).
Explains word embeddings, comparing count-based and context-based methods like skip-gram for converting words into dense numeric vectors.
A developer explores using deep learning and sequence-to-sequence models to train a chatbot on personal social media data to mimic their conversational style.
A deep dive into applying deep learning techniques to Natural Language Processing (NLP), covering word vectors and research paper summaries.
Explains the word2vec algorithm and the famous 'king - man + woman = queen' analogy using vector arithmetic and word co-occurrences.
Explores Visual Question Answering (VQA) as an alternative Turing Test, detailing neural network approaches using Python and Keras.
A blog post sharing the author's cover letter for an internship at iHub Research, focusing on their interest in automating hate speech detection using AI and NLP.
Explains the meaning of ACL 2014 peer review scores and criteria for natural language processing papers.
Authors respond to critique of their computational linguistics paper on analyzing movie characters, discussing interdisciplinary research methods.
An intuitive, probabilistic explanation of the B3 coreference resolution metric, focusing on precision and recall calculations for clustering evaluation.
