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‎404.html

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<span class="dropdown-title">Intro to PyTorch - YouTube Series</span>
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<p>Master PyTorch basics with our engaging YouTube tutorial series</p>
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</a>
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<a class="nav-dropdown-item" href="/new/">
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<span class="dropdown-title">New to PyTorch Foundation</span>
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</a>
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</div>
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</div>
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</li>
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<li>
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<a href="https://pytorch.org/tutorials/beginner/introyt.html">Introduction to PyTorch - YouTube Series</a>
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</li>
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<li>
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<a href="/new/">New to PyTorch Foundation</a>
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</li>
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</ul>
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<li class="resources-mobile-menu-title">
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<a>Ecosystem</a>

‎ai-powered-competitive-programming.html

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<span class="dropdown-title">Intro to PyTorch - YouTube Series</span>
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<p>Master PyTorch basics with our engaging YouTube tutorial series</p>
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</a>
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<a class="nav-dropdown-item" href="/new/">
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<span class="dropdown-title">New to PyTorch Foundation</span>
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</a>
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</div>
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</div>
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</li>
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<li>
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<a href="https://pytorch.org/tutorials/beginner/introyt.html">Introduction to PyTorch - YouTube Series</a>
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</li>
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<li>
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<a href="/new/">New to PyTorch Foundation</a>
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</li>
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</ul>
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<li class="resources-mobile-menu-title">
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<a>Ecosystem</a>

‎assets/hub/datvuthanh_hybridnets.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "7a19d883",
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"id": "1c9c7708",
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"metadata": {},
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"source": [
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d406f11f",
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"id": "dab33ef9",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "2e9091ed",
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"metadata": {},
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"## Model Description\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "958bb4d5",
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"metadata": {},
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},
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"cell_type": "markdown",
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"id": "02642309",
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"### Citation\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "477d3acb",
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‎assets/hub/facebookresearch_WSL-Images_resnext.ipynb

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"cells": [
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{
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"id": "2067631a",
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"id": "a7571bca",
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"metadata": {},
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d3f36c8d",
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"id": "91511522",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "465e0a99",
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"metadata": {},
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"All pre-trained models expect input images normalized in the same way,\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e591bc92",
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "17831db7",
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"metadata": {},
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},
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"cell_type": "markdown",
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"id": "402201f6",
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"metadata": {},
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"### Model Description\n",

‎assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb

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"cells": [
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"id": "80c3f9b6",
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{
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"cell_type": "code",
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},
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{
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"id": "93098203",
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"The input to the model is a noise vector of shape `(N, 120)` where `N` is the number of images to be generated.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d1d03d21",
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},
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"cell_type": "markdown",
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"id": "e44e668f",
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"You should see an image similar to the one on the left.\n",

‎assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb

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"cells": [
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"id": "ef7c6541",
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "markdown",
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"The input to the model is a noise vector of shape `(N, 512)` where `N` is the number of images to be generated.\n",
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{
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"cell_type": "code",
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},
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{
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"cell_type": "markdown",
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"id": "8c91d3ef",
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"You should see an image similar to the one on the left.\n",

‎assets/hub/facebookresearch_pytorchvideo_resnet.ipynb

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"cells": [
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"# 3D ResNet\n",
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{
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},
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"Import remaining functions:"
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"#### Setup\n",
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"Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids."
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"#### Define input transform"
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"#### Run Inference\n",
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"Load the video and transform it to the input format required by the model."
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"#### Get Predictions"
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"### Model Description\n",

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