Merge branch 'main' into plotly-intro-tutorial

Author: yakutovicha

.gitignore

@@ -135,5 +135,6 @@ dmypy.json
 *.html
 .idea/
 drafts/
-.virtual_documents
-.zed
+.virtual_documents/
+.zed/
+.ruff_cache/

00_index.ipynb

@@ -36,6 +36,7 @@
     "\n",
     "- [Introduction to data exploration](./30_introduction_data_exploration.ipynb)\n",
     "- [Image Classification](./31_image_classification.ipynb)\n",
+    "- [Language Modeling (part 1)](./32_language_modeling_1.ipynb)\n",
     "\n",
     "# Additional Topics\n",
     "\n",

24_library_pandas.ipynb

@@ -4930,6 +4930,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "formats": "ipynb,auto:percent"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -4945,7 +4948,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

31_image_classification.ipynb

@@ -659,8 +659,15 @@
     "These methods can also simulate real-world variability, helping models generalize better. \n",
     "\n",
     "In this notebook, we explore three categories of image transformations: **geometric transformations**, **image filtering**, and **photometric transformations**.\n",
-    "The following cells contain a series of exercicies designed to help you explore the OpenCV-Python library.\n",
-    "If you are unfamiliar with a particular method, refer to the [Image Processing in OpenCV](https://docs.opencv.org/4.x/d2/d96/tutorial_py_table_of_contents_imgproc.html) documentation."
+    "The following cells contain a series of exercicies designed to help you explore the OpenCV-Python library. \n",
+    "\n",
+    "If you are unfamiliar with a particular method, refer to the [Image Processing in OpenCV](https://docs.opencv.org/4.x/d2/d96/tutorial_py_table_of_contents_imgproc.html) documentation.\n",
+    "There you can find the description of the functions needed for [Geometric transformations](https://docs.opencv.org/4.x/da/d6e/tutorial_py_geometric_transformations.html) and [image filtering](https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html).\n",
+    "Regarding photometric transformations, openCV documentation does not have a specific page for that.\n",
+    "To adjust brightness and contrast, you can read [Changing the contrast and brightness of an image!](https://docs.opencv.org/4.x/d3/dc1/tutorial_basic_linear_transform.html).\n",
+    "To adjust saturation, first convert the image to the HSV color space using [`cv2.cvtColor`](https://docs.opencv.org/4.x/d8/d01/group__imgproc__color__conversions.html#gaf86c09fe702ed037c03c2bc603ceab14).\n",
+    "Then, split the image into Hue, Saturation, and Value channels with [`cv2.split`](https://docs.opencv.org/4.x/df/df2/group__core__hal__interface__split.html).\n",
+    "Modify the Saturation channel as needed, merge the channels back together using [`cv2.merge`](https://docs.opencv.org/4.x/df/d2e/group__core__hal__interface__merge.html), and finally convert the image back to the RGB color space."
    ]
   },
   {