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DTSTART:20260308T100000
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DTSTART:20261101T090000
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DTSTART;TZID=America/Los_Angeles:20260105T190000
DTEND;TZID=America/Los_Angeles:20260105T200000
DTSTAMP:20260721T214335
CREATED:20260105T233127Z
LAST-MODIFIED:20260112T224822Z
UID:32059-1767639600-1767643200@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nIntroduced the motivation of learning ML/AI\nIntroduced various divisions of ML/AI\nIntroduced Kaggle\, an interface for running ML code\n\nHomework:\nSubmit to your respective google drive homework folders when you are finished! \n\nDo this simple assignment: https://colab.research.google.com/drive/1qjmmP4LhSIgVD0qmExb_fmuB-Sm_511n\nAlso upload 2 images and load them with matplotlib at the end of the notebook.\nDownload the .ipynb file after finishing your code. Note that you can save a copy of the file so your work is persistent.\n\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/5-pm-ai-ml-darin/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260112T190000
DTEND;TZID=America/Los_Angeles:20260112T200000
DTSTAMP:20260721T214335
CREATED:20260113T051833Z
LAST-MODIFIED:20260113T051833Z
UID:32137-1768244400-1768248000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nWorked on the first project: dog and cat classification\nGot introduced to various libraries for the training stack\nLearnt about the technicalities of images\n\nHomework:\nSubmit to your respective google drive homework folders when you are finished! \n\nFigure out which two popular dog breeds from list below that DO NOT exists in the Oxford IIIT Pet dataset. YOU HAVE TO WRITE CODE TO FIND THIS. YOU CANNOT JUST GUESS. Hint look at the existing codes where we print the “Abyssinian” cat. You need to modify the code to figure out which of dog breed below that does not exists\n\namerican Pit Bull\nchihuahua\nhusky\nshiba Inu\nsamoyed\ndachshund\npomeranian\nbeagle\nboxer\npug\n\n\nWrite your answers in the latest Jan12_CatVSDog.ipynb file we worked on in a text module and explain how you found the solution.\nDownload the .ipynb file after finishing your code. Note that you can save a copy of the file so your work is persistent.\n\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/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260119T190000
DTEND;TZID=America/Los_Angeles:20260119T200000
DTSTAMP:20260721T214335
CREATED:20260120T040741Z
LAST-MODIFIED:20260120T040741Z
UID:32213-1768849200-1768852800@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nContinued on the first project: dog and cat classification\nLearnt about the labelling function\, and commenced training with a GPU for classification\n\nHomework:\nRun training again like we did in class\, but this time using 3 different AI backbones (replacing the ‘resnet34’ under the vision_learner function call). \nLook up the different AI backbones from these links (just try around like resnet50\, resnet18\, or even ulearner etc): \nhttps://docs.pytorch.org/vision/stable/models.html#initializing-pre-trained-models\nhttps://docs.fast.ai/vision.learner.html#vision_learner \nThen make your dataset have at least 10 images (I recommend looking for pictures of cats that look like dogs\, and dogs that look like cats)\, and test these images with your newly trained models. \nWrite in the same file Jan12_CatVSDog.ipynb as to which AI backbone you think is better in a text box. Upload this file to google drive when you are done. \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-2/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260126T190000
DTEND;TZID=America/Los_Angeles:20260126T200000
DTSTAMP:20260721T214335
CREATED:20260127T051814Z
LAST-MODIFIED:20260127T051814Z
UID:32291-1769454000-1769457600@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nLearnt a bit about tensors\, pre-trained models\, model sizes\nExported our trained AI model to a .pkl file for use outside of kaggle\nRan a quick streamlit app to visualize model working on the browser\n\nHomework:\n\nRun training again like we did in class\, but this time using 3 different AI backbones (replacing the ‘resnet34’ under the vision_learner function call) like last week.\nReferences for model types: \nhttps://docs.pytorch.org/vision/stable/models.html#initializing-pre-trained-models\nhttps://docs.fast.ai/vision.learner.html#vision_learner\nSAVE these models as .pkl files and put them in your pycharm project directory.\nFinally modify the pycharm file to be able to select between different models you have trained for evaluation.\nYou can add this section beneath the “created by” code to replace the load_learner for multi model selection functionality. \n\nMODELS_DIR = Path("models")\n\n# Find all .pkl files in models/\nmodel_paths = sorted(MODELS_DIR.glob("*.pkl"))\nmodel_names = [p.name for p in model_paths]\n\nif not model_paths:\n    st.error(f"No .pkl models found in: {MODELS_DIR.resolve()}")\n    st.stop()\n\n# UI: choose which model to use\nselected_name = st.selectbox("Select a model to use:"\, model_names)\n\n@st.cache_resource  # cache the loaded learner per selected model\ndef get_model(model_path_str: str):\n    return load_learner(model_path_str)\n\nmodel_path = str(MODELS_DIR / selected_name)\ncat_vs_dog_model = get_model(model_path)\n\nst.caption(f"Loaded model: {selected_name}")\n\n\nRUN your app with “streamlit run <python file path>” such as “streamlit run src/Jan26_CatVSDog_Evaluation.py”\n\nUpload the file Jan26_CatVSDog_Evaluation.py to the google drive when you are done. \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-3/
CATEGORIES:AI/ML,Python Class
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