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DTSTART;TZID=America/Los_Angeles:20260504T190000
DTEND;TZID=America/Los_Angeles:20260504T200000
DTSTAMP:20260721T053506
CREATED:20260505T000550Z
LAST-MODIFIED:20260505T000550Z
UID:33374-1777921200-1777924800@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\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-25/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260506T190000
DTEND;TZID=America/Los_Angeles:20260506T200000
DTSTAMP:20260721T053506
CREATED:20260507T030840Z
LAST-MODIFIED:20260507T030840Z
UID:33401-1778094000-1778097600@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\nContinued the Single Digit Classifier project.\n\nHomework:\nCome up with a final project! \nChoosing what you want to classify — Can be anything in real life\, the only thing is you have to make sure the data exists on Kaggle. \nThe project has to be able to classify at least 5 different categories \nFor next week: \n\nAfter picking the data for classification\, load the data into a dataloader. You don’t need a perfect labelling function yet.\nKeep a link to the dataset as well as your personal kaggle project. SET the project to public view then share the link to the project to me via email.\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-26/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260513T190000
DTEND;TZID=America/Los_Angeles:20260513T200000
DTSTAMP:20260721T053506
CREATED:20260514T030143Z
LAST-MODIFIED:20260514T030143Z
UID:33501-1778698800-1778702400@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\n\n\nFinished the Single Digit Classifier project.\n\nHomework:\nCome up with a final project if you haven’t already! \nChoosing what you want to classify — Can be anything in real life\, the only thing is you have to make sure the data exists on Kaggle. \nThe project has to be able to classify at least 5 different categories \nFor next week: \n\nSet up the labelling function\nVerify Dataloader works with .show_batch() (reference old code for how to do this\, it will depend on how you extract labels\, whether its via path or filename)\nRun training with a vision_learner.\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-27/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260520T190000
DTEND;TZID=America/Los_Angeles:20260520T200000
DTSTAMP:20260721T053506
CREATED:20260521T034953Z
LAST-MODIFIED:20260521T040109Z
UID:33607-1779303600-1779307200@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\n\n\nFinished the Single Digit Classifier project.\n\nHomework:\nFor Rexford\, choose the dataset for your project (has to be multiclass\, and catch up the below) \n\nHW 1: \n\nChoosing what you want to classify — Can be anything in real life\, the only thing is you have to make sure the data exists on Kaggle. \nThe project has to be able to classify at least 5 different categories. \nWhat we did last week: \n\nSet up the labelling function\nVerify Dataloader works with .show_batch() (reference old code for how to do this\, it will depend on how you extract labels\, whether its via path or filename)\nRun training with a vision_learner.\n\nWhat you have to do next week: \n\nUse the proper imports (fast ai 2.7.19)\nUse lr.find() to get the proper learning rate\, then run finetuning with the discovered value (you must only call fine_tune once!)\nAdd the following into the vision learner to ensure lr_find works!:\npath=Path(“/kaggle/working”)\,\nmodel_dir=”models”\,\nExport the file as a .pt file onto your computer!\n\n\nHW 2: \nDownload the following CSV file: https://drive.google.com/file/d/1kP6A9y0UBssOg3Exunv9Mnmilb0657Sh/view \nThen do the following in a new notebook called WED-7PM-PandasHW1: \n\nLoad the data \nShow only the Channel and Subscribers columns \nFind channels with more than 2000 subscribers\nFor this you can use something like this:  \ndf[df[“Math”] > 80]\,but of course for this data. \n \nAdd a new column Subs_per_Video \nAnswer the question in Markdown\, which channel is the most efficient?\n\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-28/
CATEGORIES:AI/ML,Python Class
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T190000
DTEND;TZID=America/Los_Angeles:20260527T200000
DTSTAMP:20260721T053506
CREATED:20260528T032207Z
LAST-MODIFIED:20260604T021216Z
UID:33669-1779908400-1779912000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\n\n\nFinished the Single Digit Classifier project.\n\nHomework:\nFor Rexford\, choose the dataset for your project (has to be multiclass\, and catch up the below) \n\nHW 1: \n\nChoosing what you want to classify — Can be anything in real life\, the only thing is you have to make sure the data exists on Kaggle. \nThe project has to be able to classify at least 5 different categories. \nWhat we did last week: \n\nSet up the labelling function\nVerify Dataloader works with .show_batch() (reference old code for how to do this\, it will depend on how you extract labels\, whether its via path or filename)\nRun training with a vision_learner.\nUse the proper imports (fast ai 2.7.19)\nUse lr.find() to get the proper learning rate\, then run finetuning with the discovered value (you must only call fine_tune once!)Add the following into the vision learner to ensure lr_find works!:\npath=Path(“/kaggle/working”)\,\nmodel_dir=”models”\,\nExport the file as a .pkl file onto your computer!\n\nWhat you have to do next week: \n\nUse your exported .pt file on streamlit and deploy on the website https://streamlit.io/\n\n\nHW 2 (if you haven’t done so already): \nDownload the following CSV file: https://drive.google.com/file/d/1kP6A9y0UBssOg3Exunv9Mnmilb0657Sh/view \nThen do the following in a new notebook called WED-7PM-PandasHW1: \n\nLoad the data\nShow only the Channel and Subscribers columns\nFind channels with more than 2000 subscribers\nFor this you can use something like this:df[df[“Math”] > 80]\,but of course for this data.\nAdd a new column Subs_per_Video\nAnswer the question in Markdown\, which channel is the most efficient?\n\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-29/
CATEGORIES:AI/ML,Python Class
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