NLP
Work with text: tokens, embeddings, classification, translation and speech.
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NLP tutors
14 tutors
Named entity recognition in practice
Named entity recognition in practice
Extract people, places, organisations and custom entities from text, and evaluate the results properly79 lessonsSampleNadia Haddad$7Text classification from baseline to transformerText classification from baseline to transformer
Build text classifiers step by step, starting with a strong simple baseline and honest metrics74 lessonsSampleNadia Haddad$5Word and sentence embeddingsWord and sentence embeddings
Understand how meaning becomes vectors, compute similarity yourself and choose embeddings wisely74 lessonsSampleNadia Haddad$5Machine translation: how it works and failsMachine translation: how it works and fails
Understand how machines translate, judge translation quality and know when a human translator is needed74 lessonsSampleNadia Haddad$6Sentiment analysis done carefullySentiment analysis done carefully
Measure opinions in text without fooling yourself about sarcasm, mixed views or skewed averages59 lessonsSampleNadia Haddad$4Question answering, from extractive to generativeQuestion answering, from extractive to generative
Understand how QA systems find, read and generate answers, and how to tell when they should abstain56 lessonsSampleMateo Rojas$7Tokenisation: how text becomes numbersTokenisation: how text becomes numbers
See how models split text into tokens, why it matters for cost and context, and where it trips up56 lessonsSampleNadia HaddadFreeSpeech to text pipelinesSpeech to text pipelines
Turn recordings into accurate, timestamped transcripts and understand where speech recognition fails54 lessonsSampleNadia Haddad$5Summarisation systems and their failure modesSummarisation systems and their failure modes
Build and judge summaries that stay faithful to the source, from short notes to long reports46 lessonsSampleMateo Rojas$6How language models are pretrainedHow language models are pretrained
Understand the data, objective, scaling and stability work behind large language model pretraining45 lessonsSampleMateo Rojas$13RNNs, LSTMs and why transformers took overRNNs, LSTMs and why transformers took over
Understand recurrent networks, their gates and limits, and the real reasons attention replaced them32 lessonsSampleNikolai Sorin$7Evaluating generated textEvaluating generated text
Measure the quality of generated text with metrics, people and model judges, and know each one's limitsMateo Rojas$8Teachers who teach NLP
They wrote the lesson plans these tutors follow.
Nadia Haddad
Practical NLP: from tokens and embeddings to classification, translation and speechtokenisation, embeddings, text classification9 tutorsSampleNikolai Sorin
Architectures explained from the inside: convolutions, recurrence, attention and beyondconvolutional networks, recurrent networks, transformers9 tutorsSampleMateo Rojas
Generative models and representation learning, explained with intuition first and maths secondtransfer learning, autoencoders, GANs9 tutorsSampleMore in Machine learning
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