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Merge pull request #1059 from cindyhfls/DL_2026_final_review
Dl 2026 final review
2 parents 183709b + a87d8fa commit 3f5e5f5

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‎requirements.txt‎

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@@ -5,9 +5,9 @@ numpy>=2.0
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pandas>=2.2
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matplotlib>=3.9
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torch>=2.6
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torchvision>=0.15
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torchaudio>=2.0
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scikit-learn>=1.7
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torchvision>=0.21
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torchaudio>=2.6
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scikit-learn>=1.6
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scipy>=1.13
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Pillow>=10.0
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imageio>=2.30
@@ -38,3 +38,4 @@ textattack
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tokenizers==0.22.2
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transformers
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vibecheck
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typing_extensions

‎tutorials/W2D1_Regularization/W2D1_Tutorial1.ipynb‎

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},
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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install imageio --quiet\n",
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"!pip install imageio-ffmpeg --quiet"
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"# @title Uncomment to install dependencies\n",
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"# !pip install imageio --quiet\n",
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"# !pip install imageio-ffmpeg --quiet"
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]
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},
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{

‎tutorials/W2D1_Regularization/W2D1_Tutorial2.ipynb‎

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},
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"outputs": [],
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"source": [
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"# @title Install and check dependencies\n",
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"!pip install imageio --quiet\n",
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"!pip install imageio-ffmpeg --quiet"
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"# @title Uncomment to install dependencies\n",
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"# !pip install imageio --quiet\n",
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"# !pip install imageio-ffmpeg --quiet"
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]
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},
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{

‎tutorials/W2D2_Convnets/W2D2_Tutorial1.ipynb‎

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"feedback_prefix = \"W2D2_T1\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"execution": {}
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},
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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install Pillow --quiet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.20"
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"version": "3.9.25"
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}
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},
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"nbformat": 4,

‎tutorials/W2D2_Convnets/W2D2_Tutorial2.ipynb‎

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"feedback_prefix = \"W2D2_T2\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"execution": {}
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},
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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install Pillow --quiet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"name": "python3"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "nma-dl",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.20"
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"version": "3.9.25"
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}
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},
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"nbformat": 4,

‎tutorials/W2D2_Convnets/W2D2_Tutorial3.ipynb‎

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"execution": {}
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install facenet-pytorch torchaudio==2.2.2 torchvision==0.17.2 torch==2.2.2 numpy==1.26.4 --quiet\n",
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"!pip install Pillow>=10.2.0 --quiet"
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"# # @title Uncomment to install dependencies\n",
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"# # @markdown Install `facenet` - A model used to do facial recognition.\n",
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"# # @markdown This is an old package and requires we downgrade many others.\n",
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"# # @markdown You may be asked to restart your session the first time you run this.\n",
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"# !pip install facenet-pytorch torchvision==0.17.2 torchaudio==2.2.2 torch==2.2.2 numpy==1.26.4 --quiet\n",
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"# !pip install Pillow==10.2.0 --quiet"
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]
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},
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{
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"execution": {}
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},
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"metadata": {},
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"outputs": [],
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"# @title Import Alexnet\n",
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"# @markdown This cell gives you the `alexnet` model as well as the `input_image` and `input_batch` variables used below\n",
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"import requests, urllib\n",
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"\n",
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"# Original link: https://s3.amazonaws.com/pytorch/models/alexnet-owt-4df8aa71.pth\n",
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"state_dict = torch.hub.load_state_dict_from_url(\"https://osf.io/9dzeu/download\")\n",
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"import requests, urllib, io, os\n",
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"\n",
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"# Download and save to disk first\n",
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"weights_path = \"alexnet-owt-4df8aa71.pth\"\n",
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"if not os.path.exists(weights_path):\n",
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" print(\"Downloading weights...\")\n",
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" response = requests.get(\"https://osf.io/9dzeu/download\", allow_redirects=True)\n",
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" response.raise_for_status()\n",
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" with open(weights_path, \"wb\") as f:\n",
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" f.write(response.content)\n",
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" print(f\"Saved {len(response.content) / 1e6:.1f} MB\")\n",
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"\n",
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"# Verify it's a valid torch file before loading\n",
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"print(f\"File size on disk: {os.path.getsize(weights_path) / 1e6:.1f} MB\")\n",
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"state_dict = torch.load(weights_path, map_location=\"cpu\", weights_only=False)\n",
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"alexnet = AlexNet()\n",
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"alexnet.load_state_dict(state_dict=state_dict)\n",
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"print(\"Model loaded successfully!\")\n",
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"\n",
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"url, filename = (\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content-dl/main/tutorials/W2D2_Convnets/static/dog.jpg\", \"dog.jpg\")\n",
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"try: urllib.URLopener().retrieve(url, filename)\n",
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" std=[0.229, 0.224, 0.225]),\n",
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" ])\n",
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"input_tensor = preprocess(input_image)\n",
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"input_batch = input_tensor.unsqueeze(0) # Create a mini-batch as expected by the model\n",
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"input_batch = input_tensor.unsqueeze(0)\n",
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"\n",
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"# Move the input and model to GPU for speed if available\n",
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"if torch.cuda.is_available():\n",
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" input_batch = input_batch.cuda()\n",
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" alexnet.cuda()"
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" input_batch = input_batch.cuda()\n",
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" alexnet.cuda()"
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{
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"---\n",
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"# Summary\n",
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"\n",
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"In this tutorial, you have learned about the modern Convnets (CNNs), their architecture, and operating principles. Also, you are now familiar with the notion of *Transfer Learning*, and you have learned when to apply it. If you have time left, you will learn more about the speed vs. accuracy trade-off. In the next tutorial, we will see the modern convnets in a facial recognition task."
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"So far, you have learned about the modern Convnets (CNNs), their architecture, and operating principles. If you have time left, you can continue to learn more about the speed vs. accuracy trade-off, as well as the modern convnets in a facial recognition task."
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.20"
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"version": "3.9.25"
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"nbformat": 4,

‎tutorials/W2D3_GenerativeModelsAndDeepLearningDiscussion1/W2D3_Tutorial1.ipynb‎

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"# @title Install and check dependencies\n",
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"!pip install pytorch-pretrained-biggan --quiet\n",
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"!pip install Pillow libsixel-python --quiet"
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"# @title Uncomment to install dependencies\n",
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"# !pip install pytorch-pretrained-biggan --quiet\n",
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"# !pip install libsixel-python --quiet"
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"\n",
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"trained_conv_VarAE = ConvVAE(K=K_VAE)\n",
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"elbo_vals = train_vae(trained_conv_VarAE, train_set, n_samples=10000)\n",
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"\n",
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"print(f'Learned sigma_x is {torch.exp(trained_conv_VarAE.log_sig_x)}')\n",
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"if DEVICE != 'cpu':\n",
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" elbo_vals = train_vae(trained_conv_VarAE, train_set, n_samples=10000)\n",
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" print(f'Learned sigma_x is {torch.exp(trained_conv_VarAE.log_sig_x)}')\n",
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"# Uncomment below if you'd like to see the the training\n",
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"# curve of the evaluated ELBO loss function\n",
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"# ELBO is the loss function used to train VAEs\n",
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"# (see lecture!)\n",
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"plt.figure()\n",
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"plt.plot(elbo_vals)\n",
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"plt.xlabel('Batch #')\n",
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"plt.ylabel('ELBO')\n",
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"plt.show()"
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"# plt.figure()\n",
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"# plt.plot(elbo_vals)\n",
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"# plt.xlabel('Batch #')\n",
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"# plt.ylabel('ELBO')\n",
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"# plt.show()"
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{

‎tutorials/W2D4_DiffusionGenerativeModels/W2D4_Tutorial1.ipynb‎

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"feedback_prefix = \"W2D4_T1\""
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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install pillow --quiet\n",
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"!pip install seaborn --quiet"
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‎tutorials/W2D4_DiffusionGenerativeModels/W2D4_Tutorial2.ipynb‎

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"# @title Install and check dependencies\n",
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"!pip install pillow --quiet\n",
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"!pip install diffusers transformers tokenizers --quiet\n",
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"!pip install accelerate --quiet"
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"# @title Uncomment to install dependencies\n",
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"# !pip install diffusers transformers tokenizers --quiet\n",
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"# !pip install accelerate --quiet"
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"# Section 1: Neural Network Architecture"
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‎tutorials/W2D5_TimeSeriesAndNaturalLanguageProcessing/W2D5_Tutorial1.ipynb‎

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"outputs": [],
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"source": [
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"# @title Install dependencies\n",
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"!pip install nltk --quiet\n",
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"!pip install fasttext --quiet\n",
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"!pip install -U datasets --quiet\n",
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"!pip install -U tokenizers --quiet"
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"# @title Uncomment to install dependencies\n",
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"# !pip install nltk --quiet\n",
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"# !pip install fasttext --quiet\n",
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"# !pip install -U datasets --quiet\n",
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"# !pip install -U tokenizers==0.22.2 --quiet"
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"name": "python3"
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"display_name": "nma-dl",
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"language": "python",
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"version": "3.9.25"
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"nbformat": 4,

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