+ Computer Vision with Deep Learning

notebooks/cv/cv-qa.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computer Vision Q&A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_^m"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.10.4",
+   "language": "julia",
+   "name": "julia-1.10"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/data-science-learning-paths.ipynb

@@ -108,6 +108,27 @@
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"display: flex; align-items: start; margin-bottom: 20px; padding: 20px; border-radius: 10px;\">\n",
+    "    <img src=\"images/art-computer-vision-deep-learning.png\" alt=\"Computer Vision\" style=\"width: 30%; margin-right: 20px; border-radius: 10px;\">\n",
+    "    <div style=\"width: 65%;\">\n",
+    "        <h2>Computer Vision with Deep Learning [CVDL]</h2>\n",
+    "        <p>\n",
+    "            Over this intensive 2-day course, we wil explore the key concepts of computer vision and image machine learning. Delve into neural networks, TensorFlow, and PyTorch, and apply these tools in hands-on projects. Learn to tackle image classification, segmentation, and more with confidence and expertise. \n",
+    "        </p>\n",
+    "        <ul>\n",
+    "            <li><strong>Level:</strong> Advanced</li>\n",
+    "            <li><strong>Duration:</strong> 2 days</li>\n",
+    "            <li><strong>Prerequisites:</strong> DAP+MLP</li>\n",
+    "            <li><strong>Index notebook:</strong> <a href=\"index/cv-computer-vision-deep-learning.ipynb\">📓 Start course</a></li>\n",
+    "        </ul>\n",
+    "    </div>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

notebooks/index/cv-computer-vision-deep-learning.ipynb

@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Computer Vision with Deep Learning [CEJ]\n",
+    "\n",
+    "Over this intensive 2-day course, we will explore key concepts and tools of modern computer vision. Delve into neural networks, TensorFlow, and PyTorch, and apply these tools in hands-on projects. Learn to tackle image classification, segmentation, and more with confidence and expertise.\n",
+    "\n",
+    "<img src=\"../images/art-computer-vision-deep-learning.png\" alt=\"Course Title\" style=\"max-width: 400px; width: 100%; height: auto; border-radius: 10px;\">\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chapters\n",
+    "\n",
+    "1. [**Why Deep Learning?**](../cv/cv-why-deep-learning.ipynb)<br>\n",
+    "   Introduction to deep learning for computer vision, with application examples.\n",
+    "\n",
+    "2. [**Introduction to Neural Networks**](../cv/cv-intro-neural-nets.ipynb)<br>\n",
+    "   Basic concepts, architecture, and application of neural networks to image processing.\n",
+    "\n",
+    "3. [**Introduction to TensorFlow & Keras**](../cv/cv-intro-tf-keras.ipynb)<br>\n",
+    "   Overview of TensorFlow and Keras, with hands-on neural network training.\n",
+    "\n",
+    "4. [**Convolutional Neural Networks**](../cv/cv-cnn.ipynb)<br>\n",
+    "   Motivation, architecture, and application of CNNs to image data.\n",
+    "\n",
+    "   1. [**Exercise: Image Classification**](../cv/cv-ex-image-class.ipynb)<br>\n",
+    "      Practical exercise on image classification with CNNs and TensorFlow.\n",
+    "\n",
+    "5. [**Image Augmentation Techniques**](../cv/cv-data-aug.ipynb)<br>\n",
+    "   Data augmentation techniques for improving model performance.\n",
+    "\n",
+    "6. [**Transfer Learning for Image Classification**](../cv/cv-transfer-learning.ipynb)<br>\n",
+    "   Using pre-trained models and fine-tuning for specific image classification tasks.\n",
+    "\n",
+    "   1. [**Exercise: Pre-trained Models for Image Classification**](../cv/cv-ex-pretrained.ipynb)<br>\n",
+    "      Practical exercise on transfer learning for image classification.\n",
+    "\n",
+    "7. [**Explainable Image Classification**](../cv/cv-explainable.ipynb)<br>\n",
+    "   Methods for explainable AI in image classification, with practical examples.\n",
+    "\n",
+    "8. [**Introduction to PyTorch**](../cv/cv-intro-pytorch.ipynb)<br>\n",
+    "   Overview of PyTorch, comparison to TensorFlow & Keras, hands-on introduction.\n",
+    "\n",
+    "9. [**TensorFlow vs PyTorch**](../cv/cv-tf-vs-pytorch.ipynb)<br>\n",
+    "   Comparison of TensorFlow and PyTorch frameworks, with decision support for projects.\n",
+    "\n",
+    "10. [**Object Detection with YOLO**](../cv/cv-yolo.ipynb)<br>\n",
+    "    YOLO architecture and application for object detection, with live demo.\n",
+    "\n",
+    "    1. [**Exercise: Fine-tuning an Object Detection Model**](../cv/cv-ex-yolo.ipynb)<br>\n",
+    "       Practical exercise on fine-tuning a YOLO model for object detection.\n",
+    "\n",
+    "11. [**Image Segmentation**](../cv/cv-segmentation.ipynb)<br>\n",
+    "    Overview of segmentation methods and application of CNNs for segmentation.\n",
+    "\n",
+    "12. [**Image Retrieval**](../cv/cv-retrieval.ipynb)<br>\n",
+    "    Image retrieval with embeddings, with practical application example.\n",
+    "\n",
+    "13. [**Generative Image Models**](../cv/cv-gans-vaes.ipynb)<br>\n",
+    "    Overview of generative models, including GANs and VAEs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Best Practices\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Supplement\n",
+    "\n",
+    "\n",
+    "1. [**Q&A**](../cv/cv-qa.ipynb)<br>\n",
+    "    Additional questions and answers from the workshop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔒 Exercise Solutions\n",
+    "\n",
+    "1. [**🔒 Exercise Solutions: Julia Basics**](../julia/julia-basics-exercises-solutions.ipynb)<br>\n",
+    "2. [**🔒 Exercise Solutions: Numeric Computing**](../julia/julia-numeric-exercise-solutions.ipynb)<br>\n",
+    "3. [**🔒 Exercise Solutions: Parallel Computing**](../julia/julia-parallel-exercise-solution.ipynb)<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional Resources\n",
+    "\n",
+    "- [**Julia Test Notebook**](../julia/test-julia.ipynb)<br>\n",
+    "    Verify that your Julia stack is working.\n",
+    "    \n",
+    "- [**Jupyter Cheat Sheet**](../jupyter/cheatsheet.ipynb)<br>\n",
+    "    Some useful commands for Jupyter Notebook, mostly optional."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2024 [Point 8 GmbH](https://point-8.de)_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}