From 45a24e1d116084986f1e387c3c7c4b6efb75234b Mon Sep 17 00:00:00 2001
From: Vadim Pushtaev <pushtaev.vm@gmail.com>
Date: Wed, 1 Mar 2023 20:56:08 +0200
Subject: [PATCH] intro-to-pytorch: part 3, links fix

---
 .../Part 3 - Training Neural Networks (Exercises).ipynb     | 6 +++---
 .../Part 3 - Training Neural Networks (Solution).ipynb      | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/intro-to-pytorch/Part 3 - Training Neural Networks (Exercises).ipynb b/intro-to-pytorch/Part 3 - Training Neural Networks (Exercises).ipynb
index 3e837eac5c..9a3709fd7a 100644
--- a/intro-to-pytorch/Part 3 - Training Neural Networks (Exercises).ipynb	
+++ b/intro-to-pytorch/Part 3 - Training Neural Networks (Exercises).ipynb	
@@ -64,7 +64,7 @@
     "\n",
     "Let's start by seeing how we calculate the loss with PyTorch. Through the `nn` module, PyTorch provides losses such as the cross-entropy loss (`nn.CrossEntropyLoss`). You'll usually see the loss assigned to `criterion`. As noted in the last part, with a classification problem such as MNIST, we're using the softmax function to predict class probabilities. With a softmax output, you want to use cross-entropy as the loss. To actually calculate the loss, you first define the criterion then pass in the output of your network and the correct labels.\n",
     "\n",
-    "Something really important to note here. Looking at [the documentation for `nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/nn.html#torch.nn.CrossEntropyLoss),\n",
+    "Something really important to note here. Looking at [the documentation for `nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss),\n",
     "\n",
     "> This criterion combines `nn.LogSoftmax()` and `nn.NLLLoss()` in one single class.\n",
     ">\n",
@@ -505,7 +505,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -519,7 +519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb b/intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb
index 20f6525171..3f38a15f71 100644
--- a/intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb	
+++ b/intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb	
@@ -64,7 +64,7 @@
     "\n",
     "Let's start by seeing how we calculate the loss with PyTorch. Through the `nn` module, PyTorch provides losses such as the cross-entropy loss (`nn.CrossEntropyLoss`). You'll usually see the loss assigned to `criterion`. As noted in the last part, with a classification problem such as MNIST, we're using the softmax function to predict class probabilities. With a softmax output, you want to use cross-entropy as the loss. To actually calculate the loss, you first define the criterion then pass in the output of your network and the correct labels.\n",
     "\n",
-    "Something really important to note here. Looking at [the documentation for `nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/nn.html#torch.nn.CrossEntropyLoss),\n",
+    "Something really important to note here. Looking at [the documentation for `nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss),\n",
     "\n",
     "> This criterion combines `nn.LogSoftmax()` and `nn.NLLLoss()` in one single class.\n",
     ">\n",
@@ -658,7 +658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -672,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,