update

docs/exercises/09_neural_networks1.ipynb

@@ -107,6 +107,13 @@
     "NNs can be used for regression (predict a scalar, singular, value), clustering (assigned unstructured data into groups), and many other tasks.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to learn more about Deep Learning (Deep Nerual Networks), you can check the [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) on Coursera or watch videos on [YouTube](https://youtu.be/CS4cs9xVecg?si=sULZa9qxzaqIX0M5)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/exercises/11_neural_networks3.ipynb

@@ -16,6 +16,8 @@
     "\n",
     "This tutorial demonstrates how to fine-tune a pre-trained AlexNet model on the LICS dataset, which consists of volcanic InSAR (Interferometric Synthetic Aperture Radar) images. \n",
     "\n",
+    "As you recall, AlexNet is a convolutional neural network that won the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC). You can watch this video to learn more about the AlexNet model: [link](https://youtu.be/UZDiGooFs54?si=TY_NnNt9O_fa6R-f).\n",
+    "\n",
     "The dataset contains 6,808 images, divided into two categories: 3,605 images displaying deformation signals (class 1 'volcano_deformation') and 3,203 images without deformation signals (class 0 'background_noise'). Originally, the pre-trained AlexNet was designed to classify 1,000 types of natural objects, such as tables, cars, dogs, etc. \n",
     "\n",
     "In this tutorial, we will modify the final layer of the neural network to classify images into just two classes instead of the original 1,000.\n",

docs/lectures/11_neural_networks3.ipynb

@@ -16,6 +16,8 @@
     "\n",
     "This tutorial demonstrates how to fine-tune a pre-trained AlexNet model on the LICS dataset, which consists of volcanic InSAR (Interferometric Synthetic Aperture Radar) images. \n",
     "\n",
+    "As you recall, AlexNet is a convolutional neural network that won the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC). You can watch this video to learn more about the AlexNet model: [link](https://youtu.be/UZDiGooFs54?si=TY_NnNt9O_fa6R-f).\n",
+    "\n",
     "The dataset contains 6,808 images, divided into two categories: 3,605 images displaying deformation signals (class 1 'volcano_deformation') and 3,203 images without deformation signals (class 0 'background_noise'). Originally, the pre-trained AlexNet was designed to classify 1,000 types of natural objects, such as tables, cars, dogs, etc. \n",
     "\n",
     "In this tutorial, we will modify the final layer of the neural network to classify images into just two classes instead of the original 1,000.\n",