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Code and written solutions of the assignments of the Stanford CS224N: Natural Language Processing with Deep Learning course from winter 2022/2023
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| 2026-07-31 | 277 |
| 2026-08-04 | 278 |
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# <p align="center">CS224N: Natural Language Processing with Deep Learning</p> ## <p align="center">[Stanford / Winter 2023](http://web.stanford.edu/class/cs224n/index.html)</p> This repository contains my solutions of the assignments of the Stanford CS224N: Natural Language Processing with Deep Learning course from winter 2022/23. There are many other great repositories on this course but none that cover the latest assignments (winter 2022 / 2023) and contain the written and practical parts completely (state: Mai 12. 2023). This repository is intended as a learning ressource which provides answers if you are stuck. Please do yourself a favor and try it on your own first. If you come across any errors or like me to include a more expressive explanation, please let me know at [email protected]. Reading papers is an important part of this course and crucial for completing the assignments successfully. Therefore I recommend to have a look at [How to read a Paper](https://web.archive.org/web/20231216162503/https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) From Assignment 2 and onwards you will need to edit latex files for your written solutions. I can recommend this [wikibook](https://en.wikibooks.org/wiki/LaTeX) as an up-to-date, comprehensive, and accessible reference. To have git integration I would use Vscode with a LateX extension as a LateX editor. For personal use, [overleaf](https://www.overleaf.com/project) might be easier and quicker to use. For now, all assignments are completed. I will continue watching the rest of the lectures and come back in September this year to try completing one of the default projects. ## My Schedule **Apr.18.2023** - watch [Lecture 1](https://youtu.be/rmVRLeJRkl4) and [Lecture 2](https://youtu.be/gqaHkPEZAew) - read [Efficient Estimation of Word Representations in Vector Space](http://arxiv.org/pdf/1301.3781.pdf) and [Distributed Representations of Words and Phrases and their Compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) - finish [assignment 1](https://github.com/floriankark/cs224n-win2223/tree/main/a1) - go through [Python Review Session](https://colab.research.google.com/drive/1hxWtr98jXqRDs_rZLZcEmX_hUcpDLq6e?usp=sharing) ([slides](http://web.stanford.edu/class/cs224n/readings/cs224n-python-review-2023.pdf)) **Apr.19.2023** - read [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf), [Improving Distributional Similarity with Lessons Learned from Word Embeddings](http://www.aclweb.org/anthology/Q15-1016) and [Evaluation methods for unsupervised word embeddings](http://www.aclweb.org/anthology/D15-1036) - watch [Lecture 3](https://youtu.be/X0Jw4kgaFlg) and [Lecture 4](https://youtu.be/PSGIodTN3KE) **Apr.20.2023** - read [matrix calculus notes](http://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf), [Review of differential calculus](http://web.stanford.edu/class/cs224n/readings/review-differential-calculus.pdf), [CS231n notes on network architectures](http://cs231n.github.io/neural-networks-1/), [CS231n notes on backprop](http://cs231n.github.io/optimization-2/), [Derivatives, Backpropagation, and Vectorization](http://cs231n.stanford.edu/handouts/derivatives.pdf) and [Learning Representations by Backpropagating Errors](http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf) **Apr.21.2023** - read [Understanding word vectors](https://gist.github.com/aparrish/2f562e3737544cf29aaf1af30362f469) (my own suggestion, not included in original cs224n) - finish [assignment 2 written](https://github.com/floriankark/cs224n-win2223/tree/main/a2_written) **Apr.22.2023** - read "additional readings" [A Latent Variable Model Approach to PMI-based Word Embeddings](http://aclweb.org/anthology/Q16-1028), [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://transacl.org/ojs/index.php/tacl/article/viewFi
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matched fp:62aa072e1d0205ce, topic:nlp, topic:natural-language-processing, desc:natural language processing
matched fp:62aa072e1d0205ce, topic:deep-learning, topic:pytorch