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DTSTART;TZID=America/Los_Angeles:20260427T190000
DTEND;TZID=America/Los_Angeles:20260427T200000
DTSTAMP:20260430T001050
CREATED:20260428T020008Z
LAST-MODIFIED:20260428T040007Z
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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260427T190000
DTEND;TZID=America/Los_Angeles:20260427T200000
DTSTAMP:20260430T001050
CREATED:20260428T030737Z
LAST-MODIFIED:20260428T030737Z
UID:33290-1777316400-1777320000@www.ayclogic.com
SUMMARY:7 PM - AI Agent - Gamas
DESCRIPTION:Today We Did\n\nWe made survive_or_not_survive function\nWe learned\, we have to include the scenario when asking ai to evaluate the survival strategy.\nWe learned how to force AI to response in JSON format.\nWe learned how to use Python JSON built-in library.\n\nHomework\n\nCreate a new function\, create_a_new_person()\n\nInside the function ask AI to create a new person every you call the function\, which will return a JSON in the following format\n\n\n\n{\n    "name": "Gamas"\,\n    "phone": "626-476-3067"\,\n    "age": 49\,\n    "gender": "male"\n}\n\n \n\n\nIf the age is between 13 and 19 then said “Gamas you are a teenager with phone number of 626-476-3067”. If not then just say “Gamas your phone number is 626-476-3067.\nSimilar to JUDGE_SCHEMA\, you need to create PERSON_SCHEMA and pass this to the AI.\n\n\nCall the function from the main.
URL:https://www.ayclogic.com/event/7-pm-ai-agent-gamas-3/
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