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Sebastian Raschka 2024-02-08 10:35:25 -06:00 committed by GitHub
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README.md
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@ -42,7 +42,7 @@ A collection of various deep learning architectures, models, and tips for Tensor
|Title | Dataset | Description | Notebooks |
| --- | --- | --- | --- |
| Replacing Fully-Connnected by Equivalent Convolutional Layers | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/fc-to-conv.ipynb) |
| Replacing Fully-Connected by Equivalent Convolutional Layers | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/fc-to-conv.ipynb) |

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pytorch-lightning_ipynb/transformer/distilbert-finetune-last-layers.ipynb
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@ -215,7 +215,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch-lightning_ipynb/transformer/distilbert-finetune-scheduler.ipynb
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@ -231,7 +231,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-2.ipynb
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@ -180,7 +180,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-amp16.ipynb
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@ -180,7 +180,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-ampb16.ipynb
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@ -180,7 +180,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii.ipynb
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@ -213,7 +213,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch_ipynb/cnn/cnn-vgg16-cats-dogs.ipynb
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@ -277,7 +277,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting mean and standard devation for normalizing images via z-score normalization. For details, see the related notebook [./cnn-standardized.ipynb](cnn-standardized.ipynb)."
"Getting mean and standard deviation for normalizing images via z-score normalization. For details, see the related notebook [./cnn-standardized.ipynb](cnn-standardized.ipynb)."
]
},
{

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pytorch_ipynb/transformer/1_distilbert-as-feature-extractor.ipynb
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@ -13,7 +13,7 @@
"id": "bb9d0299-8fc0-48f0-9b02-4c19214d479a",
"metadata": {},
"source": [
"In this feature-based approach, we are using the embeddings from a pretrained transormer to train a random forest and logistic regression model in scikit-learn:\n",
"In this feature-based approach, we are using the embeddings from a pretrained transformer to train a random forest and logistic regression model in scikit-learn:\n",
"\n",
"![](figures/feature-extractor.jpeg)"
]
@ -218,7 +218,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

4
pytorch_ipynb/transformer/1_distilbert-as-feature-extractor_without-dcollate.ipynb
View File

@ -13,7 +13,7 @@
"id": "bb9d0299-8fc0-48f0-9b02-4c19214d479a",
"metadata": {},
"source": [
"In this feature-based approach, we are using the embeddings from a pretrained transormer to train a random forest and logistic regression model in scikit-learn:\n",
"In this feature-based approach, we are using the embeddings from a pretrained transformer to train a random forest and logistic regression model in scikit-learn:\n",
"\n",
"![](figures/feature-extractor.jpeg)"
]
@ -212,7 +212,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

4
pytorch_ipynb/transformer/distilbert-embetter-feature-extractor.ipynb
View File

@ -13,7 +13,7 @@
"id": "2c384b90-6074-4a96-b84d-3f1f29e10b7f",
"metadata": {},
"source": [
"In this feature-based approach, we are using the embeddings from a pretrained transormer to train a random forest and logistic regression model in scikit-learn:\n",
"In this feature-based approach, we are using the embeddings from a pretrained transformer to train a random forest and logistic regression model in scikit-learn:\n",
"\n",
"![](figures/feature-extractor.jpeg)"
]
@ -230,7 +230,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch_ipynb/transformer/distilbert-hf-finetuning.ipynb
View File

@ -215,7 +215,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch_ipynb/transformer/distilbert-hf-finetuning_without-dcollate.ipynb
View File

@ -215,7 +215,7 @@
"source": [
"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
"\n",
"A) If you are working with Linux or MacOS X, open a new terminal windowm cd into the download directory and execute\n",
"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
"\n",
" tar -zxf aclImdb_v1.tar.gz\n",
"\n",

2
pytorch_ipynb/tricks/cyclical-learning-rate.ipynb
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@ -73,7 +73,7 @@
"This notebook will go over the following topics in the order listed below:\n",
"\n",
"1. Briefly explain the concept behind the cyclical learning rate\n",
"2. Use the \"LR range test\" to choose a good base and max learning rate for the cyclical leraning rate\n",
"2. Use the \"LR range test\" to choose a good base and max learning rate for the cyclical learning rate\n",
"3. Train a simple convolutional neural net on CIFAR-10 using the cyclical learning rate"
]
},

2
pytorch_ipynb/viz/cnns/cats-and-dogs/_cnn-basemodel__vgg16-cats-dogs.ipynb
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@ -277,7 +277,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting mean and standard devation for normalizing images via z-score normalization. For details, see the related notebook [./cnn-standardized.ipynb](cnn-standardized.ipynb)."
"Getting mean and standard deviation for normalizing images via z-score normalization. For details, see the related notebook [./cnn-standardized.ipynb](cnn-standardized.ipynb)."
]
},
{