yandexdataschool/nlp_course
Nlp Course
YSDA course in Natural Language Processing
Features
- Natural Language Processing - Provides a comprehensive educational curriculum covering NLP from word embeddings to dialogue systems.
- Natural Language Processing Resources - Teaches NLP concepts from word embeddings to dialogue systems through structured assignments and lectures.
- Homework Assignments - Provides graded homework assignments that apply NLP techniques to real-world problems.
- Educational Implementations - Provides hands-on assignments for building and visualizing n-gram and neural language models.
- Dialogue Systems - Teaches construction of task-oriented and open-domain conversational agents using retrieval and generative approaches.
- Curricula - Teaches natural language processing through a structured course covering foundational embeddings through modern dialogue systems.
- Educational Implementations - Provides assignments for training n-gram and neural language models from scratch.
- Sequence-to-Sequence Translation Tasks - Teaches building encoder-decoder models with attention mechanisms for translating text between languages.
- Seq2Seq Attention Models - Implements encoder-decoder architectures with attention mechanisms for sequence translation.
- Conversational Dialogue Systems - Constructs task-oriented and open-domain conversational agents using retrieval and generative approaches.
- Word Embeddings - Teaches distributional semantics and vector representations using LSA, Word2Vec, and GloVe.
- Neural Machine Translation - Implements encoder-decoder models with attention mechanisms for machine translation tasks.
- Part-of-Speech Taggers - Teaches part-of-speech tagging using structured perceptron and dynamic oracles.
- Text Classification - Applies convolutional and recurrent networks to categorize text for sentiment or topic prediction.
- Text Classifiers - Applies convolutional and recurrent neural networks to categorize text.
- Word Alignment Extractions - Learns word alignments between parallel texts using expectation-maximization algorithms.
- Dialogue Systems - Constructs task-oriented and open-domain conversational agents using retrieval and generative approaches.
- Python Data Science Courses - Teaches NLP using Python with NumPy, PyTorch, and NLTK for all assignments and examples.
- Programming Assignments - Provides weekly programming assignments that implement core NLP algorithms from scratch.
- Scaffolded Implementations - Guides students through implementing complex algorithms with pre-written stubs and tests.
- Weekly Module Progressions - Builds knowledge progressively through weekly modules from embeddings to dialogue systems.
- Jupyter Notebook Curricula - Delivers interactive code cells and visualizations in the browser for experimenting with NLP models.
- Lecture-Notebook Pairs - Pairs each lecture topic with a Jupyter notebook for immediate hands-on practice.
- Blog-Style Lectures - Delivers lecture content as intuitive, engaging blog-style pages that are complete and up-to-date.
- NLP Lecture Slides - Provides structured lecture slides covering core natural language processing topics.
- Programming Assignments - Provides weekly programming assignments that implement core NLP algorithms from scratch.
- Algorithm Scaffolding - Provides pre-written stubs and tests for implementing attention mechanisms and structured perceptrons.
- Document Summarization - Produces concise summaries of longer texts using extractive or abstractive methods.
- Word Alignment - Learns word alignments between parallel corpora using EM algorithms for translation tasks.
- Educational Explanations - Includes dedicated analysis sections in lectures to explain model internals.
- Text Summarization - Produces concise summaries by selecting key sentences or generating new text from source documents.
- Python NLP Libraries - Teaches NLP algorithms using Python with NumPy, PyTorch, and NLTK for all assignments and examples.
- Educational Curricula - Teaches NLP algorithms using Python with NumPy, PyTorch, and NLTK for all assignments and examples.
- Domain Adaptation Techniques - Adapts a general machine translation model to a specific domain by matching features and distilling knowledge.
- NLP Seminar Exercises - Offers seminar materials with hands-on exercises to reinforce NLP concepts taught in lectures.
- Paper Summaries - Summarizes key results and reasoning from top NLP papers for quick understanding.
- Summaries - Summarizes key results and reasoning from influential NLP papers.
- Research Skill Development - Guides learners to identify flaws, hypothesize improvements, and explore prior work.
