course-nlp
nlp_graph
| course-nlp | nlp_graph | |
|---|---|---|
| 11 | 1 | |
| 3,390 | 1 | |
| 0.0% | - | |
| 0.0 | 2.6 | |
| about 1 year ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | - |
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course-nlp
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Need help finding good NLP course
fast.ai also has an NLP course.
- I got a crazy idea how about all the programs here work on making Ms.J a reality
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A simple and effective way to go from beginner to intermediate level of ML knowledge
fastai already has a course that covers traditional nlp as well
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Would I be better off with OMSCS or online self-learning?
My question: How do you think high-quality online resources such as https://missing.csail.mit.edu/, https://fullstackopen.com/en/, http://allendowney.github.io/ThinkBayes2/, https://www.fast.ai/2019/07/08/fastai-nlp/ compare to the online education from OMSCS? Do any of my reasons above ring false for you?
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Suggestions on My Learning Path
8 - Code-First Introduction to Natural Language Processing - fast.ai
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Ex-Googlers raise $40M to democratize natural-language AI
fast.ai are doing the best work democratizing AI (machine learning and neural networks), including natural language
Can't recommend the course enough and it's incredible that such a quality resource is available for essentially zero cost
https://github.com/fastai/course-nlp
https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQju...
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Anyone have deep learning textbook recommendations, specifically focused on NLP?
fastai nlp course
- Good NLP Course
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Matrix Factorization Approaches to Topic Modeling
[1] Chapter on LSI from Introduction to Information Retrieval [2] A Code-First Approach to Natural Language Processing by fast.ai [3] Computational Linear Algebra by fast.ai [4] https://www.cc.gatech.edu/~hpark/papers/nmf_book_chapter.pdf
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OPEN SOURCE COMPUTER SCIENCE CURRICULUM
NLP
nlp_graph
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graph_summarizer
Little prehistory: some time ago I worked on graph visualizations based on vector embedding of texts . This vizualisations show relational "distance" between words of sentences. So user can visualize graph of distances between word of text, also you can apply some advanced graph-theoretic approaches to further analyze input text and it's hidden features. I want to use this approach to try on text summarization problem - in my case to extract some more informative sentences from text to compress original text.
What are some alternatives?
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
adjustText - A small library for automatically adjustment of text position in matplotlib plots to minimize overlaps.
eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions
community-vaccines - Could Graph Optimizations Help Deliver Donated Covid Vaccines Overseas PreExpiry?
argos-translate - Open-source offline translation library written in Python
ML-Workspace - 🛠All-in-one web-based IDE specialized for machine learning and data science.
cornell-cs5785-2020-applied-ml - Teaching materials for the applied machine learning course at Cornell Tech (online edition)
nlp_course - YSDA course in Natural Language Processing
medspacy - Library for clinical NLP with spaCy.
numerical-linear-algebra - Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
huggingface_hub - The official Python client for the Huggingface Hub.
missing-semester - The Missing Semester of Your CS Education 📚