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Learn how to use PyTorch to solve some common NLP problems with deep learning.
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| 2026-07-31 | 415 |
| 2026-08-06 | 415 |
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# A Deep Dive into NLP with PyTorch Learn how to use PyTorch to solve some common NLP problems with deep learning. View these notebooks on [nbviewer](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/tree/develop/). - [`1_BoW_text_classification.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/1_BoW_text_classification.ipynb): Train a bag-of-words model to predict the sentiment of IMDB reviews - [`2_embeddings.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/2_embeddings.ipynb): Play around with different pretrained word embeddings - [`3_rnn_text_classification.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/3_rnn_text_classification.ipynb): Train an RNN to predict the sentiment of IMDB movie reviews - [`4_character_text_generation.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/4_character_text_generation.ipynb): Train a character-level RNN language model to generate weight loss articles - [`5_seq2seq_attention_translation.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/5_seq2seq_attention_translation.ipynb): Train an RNN-based Seq2Seq model with attention to translate from English to French - [`6_transformer_translation.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/6_transformer_translation.ipynb): Train a pure self-attention based transformer Seq2Seq model to translate from English to French - [`7_gpt2_finetuned_text_generation.ipynb`](https://nbviewer.jupyter.org/github/scoutbee/pytorch-nlp-notebooks/blob/develop/7_gpt2_finetuned_text_generation.ipynb): Fine-tune the pretrained (small) GPT-2 model to generate weight loss articles **Tutorials** | Events | Dates | Slides | | ------------- |:-------------:| -----:| | [PyData London 2019](https://pydata.org/london2019/schedule/) | 12 Jul 2019 | [link](https://docs.google.com/presentation/d/1zyuwCx7knqnP-LJswlDfWSmk5FhFgFmYJGqdEZn8yhc/edit?usp=sharing) | | [PyData Cambridge 2019](https://cambridgespark.com/pydata-cambridge-2019-schedule/) | 15 Nov 2019 | [link](https://docs.google.com/presentation/d/1W-Ar8ZQt9fGJfiFJzhQ61TZI3HgTj2swxzuh53Xh5Sk/edit?usp=sharing) | | [DS Con Belgrade 2019](https://www.datasciconference.com/technical-tutorials/) | 18 Nov 2019 | [link](https://docs.google.com/presentation/d/1b-5T6FqlJfMK-06k8rfw5e57su9mf2FwClnNZfOw8Ao/edit?usp=sharing) | ## Setup Make sure you have a Google account and visit [Google Colab](https://colab.research.google.com/github/scoutbee/pytorch-nlp-notebooks). You should see a list of notebooks pop up:  If you have trouble with that, you can also save the notebook you want to run from this repo to your local filesystem, and then upload it to Google Colab with `File -> Open Notebook -> Upload`. ### Basic Navigation You can run cells with \<SHIFT\> + \<ENTER\>. ### Missing packages If you find that you are missing a necessary package, you can prepend `!` to a bash command. For example, to install `googledrivedownloader`, you would run in a cell: ``` !pip install googledrivedownloader ``` ### Using a GPU To use a GPU (for free!), select from the top menu from Colab `Runtime -> Change Runtime Type -> Hardware Accelerator -> GPU`. Pay attention to how much memory the GPU is currently using by clicking `Runtime -> Manage Sessions`. ## Contributing Feel free to submit a PR for cleanups, error-fixing, or adding new (relevant) content!
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bbd52c4afd09c050, topic:deep-learning, topic:pytorch
matched fp:bbd52c4afd09c050, topic:nlp, readme:text classification