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DTSTART;TZID=America/Los_Angeles:20260427T190000
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UID:33288-1777316400-1777320000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nFinalized finals projects\, debugged streamlit and dataset related issues.\nFinished the Boston Housing Price Prediction!\n\nHomework:\n\nGet your app working on the streamlit website! One more thing you have to ensure is that the labeling function exists with the same name on your pycharm script\nWatch this 45 mins recording about Random Forest https://www.simplilearn.com/tutorials/machine-learning-tutorial/random-forest-algorithm\n\n  \nFor Reine\, this code is the fix\, I have tested and verified training: \npath = "/kaggle/input/datasets/vesuvius13/formula-one-cars/Formula One Cars"\n# Get every image under all team folders\nall_files = get_image_files(path)\nprint("Total files before cleaning:"\, len(all_files))\n\nfrom PIL import Image\nfrom pathlib import Path\n\ndef is_valid_image(fn):\n    try:\n        # First check the image header\n        with Image.open(fn) as im:\n            im.verify()\n\n        # Then actually load/convert it\, because verify() alone can miss some bad files\n        with Image.open(fn) as im:\n            im.convert("RGB").load()\n\n        return True\n\n    except Exception as e:\n        return False\n\ngood_files = []\nbad_files = []\n\nfor fn in all_files:\n    if is_valid_image(fn):\n        good_files.append(fn)\n    else:\n        bad_files.append(fn)\n\nprint("Good files:"\, len(good_files))\nprint("Bad files:"\, len(bad_files))\n\nfor fn in bad_files[:30]:\n    print(fn)\ndef extract_brand(fn):\n    folder_name = Path(fn).parent.name\n    return folder_name.replace(" F1 car"\, "").strip()\n\nprint(extract_brand("/kaggle/input/datasets/vesuvius13/formula-one-cars/Formula One Cars/Racing Point F1 car/00000090.png"))\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-24/
CATEGORIES:AI/ML,Python Class
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