BEGIN:VCALENDAR VERSION:2.0 PRODID:-//American Young Coder - ECPv6.10.1.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:American Young Coder X-ORIGINAL-URL:https://www.ayclogic.com X-WR-CALDESC:Events for American Young Coder REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20260308T100000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20261101T090000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260715T190000 DTEND;TZID=America/Los_Angeles:20260715T200000 DTSTAMP:20260721T101355 CREATED:20260716T075317Z LAST-MODIFIED:20260716T075317Z UID:34166-1784142000-1784145600@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\n\n\nContinued the Boston Housing Prediction project!\n\nHomework:\n\n\n\n\n\n\nWatch this video: https://www.youtube.com/watch?v=1aM1KYvl4Dw \n\n\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-34/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260708T190000 DTEND;TZID=America/Los_Angeles:20260708T200000 DTSTAMP:20260721T101355 CREATED:20260716T010622Z LAST-MODIFIED:20260716T010622Z UID:34162-1783537200-1783540800@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\n\n\nStarted the Boston Housing Prediction project\n\nHomework:\n\n\n\n\n\n\nWatch this video by next week! https://www.ayclogic.com/event/7-pm-ai-machine-learning-gamas-13/ \n\n\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-33/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260610T190000 DTEND;TZID=America/Los_Angeles:20260610T200000 DTSTAMP:20260721T101355 CREATED:20260611T032816Z LAST-MODIFIED:20260611T032816Z UID:33813-1781118000-1781121600@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\n\n\nContinued the titanic survival project\n\nHomework:\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\nMake sure training used the proper installs and library versions!\nRerun exporting the .pt file and save it from the outputs on the right of the kaggle page\nUse your exported .pt file on streamlit and deploy on the website https://streamlit.io/Details are as follows below:\n\n\n\nDetails on how to deploy on streamlit:\n\nSet up a github account. \n❌ means skip unless you lost it\, ✅ means do \n\nCreate an account at https://github.com/ if you haven’t already. ❌\nGo to the top right on your avatar icon\, and click on repositories. ✅\nClick new at the top right. ✅\nCreate a new repository called AYCLOGIC_WED7PM_AI_ML_FINAL_PROJECT. Leave all other settings at default\, but make sure the visibility is set to public. ✅\nThen go to the avatar icon once more\, and this time click on settings. \nInside settings\, scroll to the very bottom and on the menu bar you should select “Developer Settings“. ❌\nOnce in Developer Settings\, click on Personal Access Tokens (Tokens Classic). ❌\nThen click on “Generate New Token (classic)”. ❌\nSet expiration to 90 days\, and tick “admin:org” and “write:packages“.\nSave the newly generated token but don’t share it! ❌\nThen go back to your pycharm and go to the terminal and follow these instructions: ✅\n\ngit init\n\ngit add .\n\ngit commit -m "Initial Commit"\n\ngit remote add origin https://github.com//AYCLOGIC_WED7PM_AI_ML_FINAL_PROJECT.git\n\ngit branch -M main\n\ngit push -u origin main\n\n#######################################################\n\nNote that for the above\, you plug in the github username you created before.\n\nUpon executing the commands\, you will be asked to login:\n1. For username\, you can type in your github username\n2. For password\, copy and paste in your personal access token.\n\nNote: When typing in the username and password\, you won't see any characters typed out which is actually a security feature\, but it is there\n\nExtra note: If you get an error saying git is unavailable\, you will need to install it. Email me if this is the case\nFinally after all this you should be able to go to streamlit.io -> top right -> free -> login with github -> choosing your own github repository -> modifying the settings to do python 3.12 -> deploy \nNote it is important you train your model with the downgraded versions (uncomment them)\, then after running the pip install proceed by hitting Run -> Restart & Clear Cell Outputs before proceeding with the other blocks. Make sure your imports match the pip installs\, then finally export the model and download it from /kaggle/working on the right side of the website (may need to hit on refresh – the circular arrows on the right of /kaggle/working) \n\n\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-31/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260603T190000 DTEND;TZID=America/Los_Angeles:20260603T200000 DTSTAMP:20260721T101355 CREATED:20260604T034205Z LAST-MODIFIED:20260604T034205Z UID:33749-1780513200-1780516800@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\n\n\nFinished the Single Digit Classifier project.\n\nHomework:\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\nMake sure training used the proper installs and library versions!\nRerun exporting the .pt file and save it from the outputs on the right of the kaggle page\nUse your exported .pt file on streamlit and deploy on the website https://streamlit.io/\nDetails are as follows below:\n\n\n\nDetails on how to deploy on streamlit:\n\nSet up a github account. \n❌ means skip unless you lost it\, ✅ means do \n\nCreate an account at https://github.com/ if you haven’t already. ❌\nGo to the top right on your avatar icon\, and click on repositories. ✅\nClick new at the top right. ✅\nCreate a new repository called AYCLOGIC_WED7PM_AI_ML_FINAL_PROJECT. Leave all other settings at default\, but make sure the visibility is set to public. ✅\nThen go to the avatar icon once more\, and this time click on settings. \nInside settings\, scroll to the very bottom and on the menu bar you should select “Developer Settings“. ❌\nOnce in Developer Settings\, click on Personal Access Tokens (Tokens Classic). ❌\nThen click on “Generate New Token (classic)”. ❌\nSet expiration to 90 days\, and tick “admin:org” and “write:packages“.\nSave the newly generated token but don’t share it! ❌\nThen go back to your pycharm and go to the terminal and follow these instructions: ✅\n\ngit init\n\ngit add .\n\ngit commit -m "Initial Commit"\n\ngit remote add origin https://github.com//AYCLOGIC_WED7PM_AI_ML_FINAL_PROJECT.git\n\ngit branch -M main\n\ngit push -u origin main\n\n#######################################################\n\nNote that for the above\, you plug in the github username you created before.\n\nUpon executing the commands\, you will be asked to login:\n1. For username\, you can type in your github username\n2. For password\, copy and paste in your personal access token.\n\nNote: When typing in the username and password\, you won't see any characters typed out which is actually a security feature\, but it is there\n\nExtra note: If you get an error saying git is unavailable\, you will need to install it. Email me if this is the case\nFinally after all this you should be able to go to streamlit.io -> top right -> free -> login with github -> choosing your own github repository -> modifying the settings to do python 3.12 -> deploy \n\n\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-30/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260527T190000 DTEND;TZID=America/Los_Angeles:20260527T200000 DTSTAMP:20260721T101355 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260520T190000 DTEND;TZID=America/Los_Angeles:20260520T200000 DTSTAMP:20260721T101355 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:20260513T190000 DTEND;TZID=America/Los_Angeles:20260513T200000 DTSTAMP:20260721T101355 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:20260506T190000 DTEND;TZID=America/Los_Angeles:20260506T200000 DTSTAMP:20260721T101355 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:20260504T190000 DTEND;TZID=America/Los_Angeles:20260504T200000 DTSTAMP:20260721T101355 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:20260427T190000 DTEND;TZID=America/Los_Angeles:20260427T200000 DTSTAMP:20260721T101355 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260422T190000 DTEND;TZID=America/Los_Angeles:20260422T200000 DTSTAMP:20260721T101355 CREATED:20260423T031304Z LAST-MODIFIED:20260423T031304Z UID:33249-1776884400-1776888000@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued the new project on Multi-class Classification\nWent over deployment code using streamlit\n\nHomework:\nUpload a screenshot of the website working (not locally) \nOn the streamlit website\, ensure you chose python 3.12 and that you have pushed the latest code to github with the commands: \ngit add .\ngit commit -m "update"\ngit push origin main\nAnd if pushing doesn’t work: \ngit push origin main --force\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-23/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260420T190000 DTEND;TZID=America/Los_Angeles:20260420T200000 DTSTAMP:20260721T101355 CREATED:20260420T231312Z LAST-MODIFIED:20260420T231312Z UID:33229-1776711600-1776715200@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nFinalized finals projects\, debugged streamlit and dataset related issues.\nContinued the Boston Housing Price Prediction!\n\nHomework:\nNOTE: please click on save version for your respective final projects on the kaggle page. \nNOTE 2: Use these versions across your kaggle and your pycharm: \n!pip install --force-reinstall --no-cache-dir numpy==1.26.4 scipy==1.15.3 matplotlib==3.7.2 fastai==2.7.19 torch==2.6.0 pillow==10.4.0\nPart 1:\n\nContinue working on your final project.Your goal this week is to train multiple models and deploy them all with the option to choose between different models on streamlit. Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \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-22/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260415T190000 DTEND;TZID=America/Los_Angeles:20260415T200000 DTSTAMP:20260721T101355 CREATED:20260416T032535Z LAST-MODIFIED:20260416T032535Z UID:33183-1776279600-1776283200@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued the new project on Multi-class Classification\nWent over deployment code using streamlit\n\nHomework:\n# Part 1\n# Make sure the streamlit deployment on the web works\n# Ensure the version match between kaggle and pycharm.\n# In the streamlit website\, set the python version to 3.12 \n# Part 2\n# Use the same code as in CatVSDog\, and train your model for multiclass classification \n# After training\, write down the “error rate” in a comment. \n# Also test your model on the custom dog dataset. \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-21/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260413T170000 DTEND;TZID=America/Los_Angeles:20260413T180000 DTSTAMP:20260721T101355 CREATED:20260415T213503Z LAST-MODIFIED:20260415T213503Z UID:33177-1776099600-1776103200@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\, debugged streamlit and dataset related issues.\nContinued the Boston Housing Price Prediction!\n\nHomework:\nNOTE: please click on save version for your respective final projects on the kaggle page. \nNOTE 2: Use these versions across your kaggle and your pycharm: \n!pip install --force-reinstall --no-cache-dir numpy==1.26.4 scipy==1.15.3 matplotlib==3.7.2 fastai==2.7.19 torch==2.6.0 pillow==10.4.0\nPart 1:\n\nContinue working on your final project.Your goal this week is to train multiple models and deploy them all with the option to choose between different models on streamlit. Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \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-20/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260408T190000 DTEND;TZID=America/Los_Angeles:20260408T200000 DTSTAMP:20260721T101355 CREATED:20260409T074439Z LAST-MODIFIED:20260409T074936Z UID:33126-1775674800-1775678400@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nStarted the new project on Multi-class Classification\nWent over deployment code using streamlit\n\nHomework:\nContinuing off of last week if you haven’t already: \nPart 1\nIn your pycharm project\, do the following: \n# hw: \n# deploy the streamlit application\n\n# you have to ensure to use these installs on kaggle:\n\n!pip install --force-reinstall --no-cache-dir \\n"numpy==1.26.4" \\n"scipy==1.15.3" \\n"matplotlib==3.7.2" \\n"fastai==2.7.19" \\n"torch==2.6.0" \\n"pillow==10.4.0"\n\nthen on your pycharm in your requirements.txt you must use:\n\n\n\nstreamlit==1.40.1\nnumpy==1.26.4\nscipy==1.15.3\nmatplotlib==3.9.2\npillow==10.4.0\nfastai==2.7.19\n\n# pip install -r requirements.txt\n\nFor the above\, use this code: link \nPart 2\nSet up a github account. \n\nCreate an account at https://github.com/ if you haven’t already.\nGo to the top right on your avatar icon\, and click on repositories.\nClick new at the top right.\nCreate a new repository called AYCLOGIC_WED7PM_AI_ML. Leave all other settings at default\, but make sure the visibility is set to public.\nThen go to the avatar icon once more\, and this time click on settings.\nInside settings\, scroll to the very bottom and on the menu bar you should select “Developer Settings“.\nOnce in Developer Settings\, click on Personal Access Tokens (Tokens Classic).\nThen click on “Generate New Token (classic)”.\nSet expiration to 90 days\, and tick “admin:org” and “write:packages“.\nSave the newly generated token but don’t share it!\nThen go back to your pycharm and go to the terminal and follow these instructions:\n\ngit init\n\ngit add .\n\ngit commit -m "Initial Commit"\n\ngit remote add origin https://github.com//AYCLOGIC_WED7PM_AI_ML.git\n\ngit branch -M main\n\ngit push -u origin main\n\n#######################################################\n\nNote that for the above\, you plug in the github username you created before.\n\nUpon executing the commands\, you will be asked to login:\n1. For username\, you can type in your github username\n2. For password\, copy and paste in your personal access token.\n\nNote: When typing in the username and password\, you won't see any characters typed out which is actually a security feature\, but it is there\n\nExtra note: If you get an error saying git is unavailable\, you will need to install it. Email me if this is the case\n\n\n\nPart 3\nDeploy your application on streamlit online: https://streamlit.io/ \n\nOn the top right click free -> deploy with streamlit community cloud-> log in with github\nProceed to login with your github account\nHit on create app\nUse the github repository you have created and deploy!\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-17/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260406T190000 DTEND;TZID=America/Los_Angeles:20260406T200000 DTSTAMP:20260721T101355 CREATED:20260406T234352Z LAST-MODIFIED:20260407T065604Z UID:33088-1775502000-1775505600@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\, debugged streamlit and dataset related issues.\nFinished the Titanic Survival Rate Prediction Problem!\n\nHomework:\nNOTE: please click on save version for your respective final projects on the kaggle page. \nNOTE 2: Use these versions across your kaggle and your pycharm: \n!pip install --force-reinstall --no-cache-dir numpy==1.26.4 scipy==1.15.3 matplotlib==3.7.2 fastai==2.7.19 torch==2.6.0 pillow==10.4.0\nPart 1:\n\nContinue working on your final project.Your goal this week is to train multiple models and deploy them all with the option to choose between different models on streamlit. Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \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-19/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260401T190000 DTEND;TZID=America/Los_Angeles:20260401T200000 DTSTAMP:20260721T101355 CREATED:20260402T103452Z LAST-MODIFIED:20260409T020130Z UID:33059-1775070000-1775073600@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued the first project on Cat vs Dog classification\, doing deployment outside of Kaggle.\n\nHomework:\nPart 1\nIn your pycharm project\, do the following: \n# hw: \n# deploy the streamlit application\nFor the above\, use this code: link \nPart 2\nSet up a github account. \n\nCreate an account at https://github.com/ if you haven’t already.\nGo to the top right on your avatar icon\, and click on repositories.\nClick new at the top right.\nCreate a new repository called AYCLOGIC_WED7PM_AI_ML. Leave all other settings at default\, but make sure the visibility is set to public.\nThen go to the avatar icon once more\, and this time click on settings.\nInside settings\, scroll to the very bottom and on the menu bar you should select “Developer Settings“.\nOnce in Developer Settings\, click on Personal Access Tokens (Tokens Classic).\nThen click on “Generate New Token (classic)”.\nSet expiration to 90 days\, and tick “admin:org” and “write:packages“.\nSave the newly generated token but don’t share it!\nThen go back to your pycharm and go to the terminal and follow these instructions:\n\ngit init\n\ngit add .\n\ngit commit -m "Initial Commit"\n\ngit remote add origin https://github.com//AYCLOGIC_WED7PM_AI_ML.git\n\ngit branch -M main\n\ngit push -u origin main\n\n#######################################################\n\nNote that for the above\, you plug in the github username you created before.\n\nUpon executing the commands\, you will be asked to login:\n1. For username\, you can type in your github username\n2. For password\, copy and paste in your personal access token.\n\nNote: When typing in the username and password\, you won't see any characters typed out which is actually a security feature\, but it is there\n\nExtra note: If you get an error saying git is unavailable\, you will need to install it. Email me if this is the case\n\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-18/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260330T190000 DTEND;TZID=America/Los_Angeles:20260330T200000 DTSTAMP:20260721T101355 CREATED:20260330T235041Z LAST-MODIFIED:20260331T062753Z UID:33029-1774897200-1774900800@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\, debugged streamlit and dataset related issues.\nFinished the Titanic Survival Rate Prediction Problem!\n\nHomework:\nPart 1:\n\nContinue working on your final project.\nYour goal this week is to train multiple models and deploy them all with the option to choose between different models on streamlit. Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \nPart 2:\n\nAdd some code in the Titanic Survival Rate project.\n\nCount how many passengers are male and how many are female. Use “Sex” column.\nFigure out the most expensive ticket price and the cheapest and the average. Use “Fare” column.\nFigure out how many people are younger than 10 years old. Use “Age” column.\n\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-16/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260323T190000 DTEND;TZID=America/Los_Angeles:20260323T200000 DTSTAMP:20260721T101355 CREATED:20260323T215733Z LAST-MODIFIED:20260324T031751Z UID:32953-1774292400-1774296000@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\nContinued the Titanic Survival Rate Prediction Problem\n\nHomework:\nFor everyone: \n\nContinue working on your final project. You should have a model trained already at this point\, and you want to get streamlit up and running for testing model deployment! Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \n  \nAlso in the latest titanic surival project\, jot down one observation you have on what the model learned or some trend in the data (for example in class we observed that higher ages lead to lower survival rates) \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-14/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260318T190000 DTEND;TZID=America/Los_Angeles:20260318T200000 DTSTAMP:20260721T101355 CREATED:20260320T011814Z LAST-MODIFIED:20260320T011814Z UID:32915-1773860400-1773864000@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued the first project on Cat vs Dog classification\nLearned more on how training an AI model in a supervised way works.\n\nHomework:\nIn your wed-mar4-catvsdog kaggle notebook\, do the following: \n# hw: \n# Ensure your individual custom dataset images have capitalization of the first letter of the file name\n\n# run prediction on each image in ur custom dataset then\nsee if there are any errors\, and print out the number of correct guesses\n\n# you can use the labeling function to test correctness.\nso something like:\nif predicted_ans == true_ans:\n corrects += 1\n\n# hint: run a for loop over the list of your custom images\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-13/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260311T190000 DTEND;TZID=America/Los_Angeles:20260311T200000 DTSTAMP:20260721T101355 CREATED:20260312T031009Z LAST-MODIFIED:20260312T031009Z UID:32837-1773255600-1773259200@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nIntroduced the training pipeline for Supervised Learning at a high level.\nIntroduced Kaggle as a platform for running ML code.\n\nHomework:\nIn your wed-mar4-catvsdog kaggle notebook\, add in 25 cat images and 25 dog images to a new dataset named “CatVSDog_Test_Images“. These should be random and not duplicates of one another. You may use photos of your own cat/dog if you have one. \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-12/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260309T190000 DTEND;TZID=America/Los_Angeles:20260309T200000 DTSTAMP:20260721T101355 CREATED:20260310T033604Z LAST-MODIFIED:20260310T035916Z UID:32805-1773082800-1773086400@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nReviewed finals projects\nReviewed using pandas\nStarted the Titanic Survival Rate Prediction Problem\n\nHomework:\nFor everyone: \n\nContinue working on your final project. You want to finish training your model! Upload your latest progress as an ipynb to the google drive.\n\nFor those with issues on training the models\, do this fix: \n!pip install -U "fastprogress==1.0.3"\nimport fastprogress\nprint("fastprogress:"\, fastprogress.__version__)\nThen ensure it is 1.0.3\, if it isn’t restart the notebook (theres a restart and clear cell output button on the top right of the notebook page). \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-11/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260304T190000 DTEND;TZID=America/Los_Angeles:20260304T200000 DTSTAMP:20260721T101355 CREATED:20260305T062853Z LAST-MODIFIED:20260305T063006Z UID:32754-1772650800-1772654400@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nIntroduced the topic of ML/AI at a high level.\nIntroduced Kaggle as a platform for running ML code.\n\nHomework:\nCopy the contents from this google colab file to kaggle\, and complete the hw\, then save as .ipynb: https://colab.research.google.com/drive/1qjmmP4LhSIgVD0qmExb_fmuB-Sm_511n \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-10/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260302T190000 DTEND;TZID=America/Los_Angeles:20260302T200000 DTSTAMP:20260721T101355 CREATED:20260303T041846Z LAST-MODIFIED:20260303T041916Z UID:32729-1772478000-1772481600@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued multi-class classification on the MNIST dataset.\nLearned about data augmentation through the batch_tfms parameter for the dataloader.\n\nHomework:\nFor Reine: \n\nCreate a google doc under your homework google drive \nWrite up your project proposal for training your own AI model detailing:\n– What you will be classifying (has to be multi-class classification!)\n– Where your data comes from (provide a link or methodology of getting the dataset). If you are using an existing dataset\, you have to add more images from online means (gather data from outside of kaggle\, whether it’s online images\, etc)\n– How you will train your AI model (what transformations on the data you will use\, and the flow of training)\nYou can view the list of transformations here: https://docs.fast.ai/vision.augment.html#aug_transforms\n\nEnsure the google doc is public access and that it is in your homework folder. \n  \nFor everyone: \n\nContinue working on your final project. You want to at least ensure you have the data loader setup with the proper transformations (also at this point it should be easy to run training once you have the dataloader). Upload your latest progress as an ipynb to the google drive.\nCreate a new kaggle notebook Mar9_PandasHW.ipynb and do the following:\nYoutube.csv  \n\nUse this csv file – https://drive.google.com/file/d/1kP6A9y0UBssOg3Exunv9Mnmilb0657Sh/view?usp=drive_link \nLoad the data\n\nShow only the Channel and Subscribers columns\n\nFind channels with more than 2000 subscribers\n\nAdd a column Subs_per_Video\n\nWhich channel is the most efficient?\n\n\n\nHW Note: you can display rows with conditions like this:\ndf_math_greater_than_80 = df[df["Math"] > 80]\nprint("\nStudents with Math Score Greater Than 80:")\nprint(df_math_greater_than_80)\nprint(df.head())\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-9/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260223T190000 DTEND;TZID=America/Los_Angeles:20260223T200000 DTSTAMP:20260721T101355 CREATED:20260224T043712Z LAST-MODIFIED:20260224T043712Z UID:32645-1771873200-1771876800@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nNo class today\, there was a technical issue with zoom links\, we will meet next week in the updated zoom link. Make sure you join the right one!\n\nHomework:\n\nBegin working on your project! You will have exactly 3 weeks to complete it\, and by the end of it you want to deploy a simple streamlit application for your project.\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-8/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260216T190000 DTEND;TZID=America/Los_Angeles:20260216T200000 DTSTAMP:20260721T101355 CREATED:20260217T043338Z LAST-MODIFIED:20260217T043554Z UID:32559-1771268400-1771272000@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued multi-class classification on the MNIST dataset.\nLearned about data augmentation through the batch_tfms parameter for the dataloader.\n\nHomework:\n\nCreate a google doc under your homework google drive \nWrite up your project proposal for training your own AI model detailing:\n– What you will be classifying (has to be multi-class classification!)\n– Where your data comes from (provide a link or methodology of getting the dataset). If you are using an existing dataset\, you have to add more images from online means (gather data from outside of kaggle\, whether it’s online images\, etc)\n– How you will train your AI model (what transformations on the data you will use\, and the flow of training)\nYou can view the list of transformations here: https://docs.fast.ai/vision.augment.html#aug_transforms\n\nEnsure the google doc is public access and that it is in your homework folder. \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-7/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260209T190000 DTEND;TZID=America/Los_Angeles:20260209T200000 DTSTAMP:20260721T101355 CREATED:20260210T043506Z LAST-MODIFIED:20260217T043259Z UID:32426-1770663600-1770667200@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nContinued multi-class classification on the MNIST dataset.\nLearned about data augmentation through the batch_tfms parameter for the dataloader.\n\nHomework:\n\nCreate a google doc under your homework google drive \nWrite up your project proposal for training your own AI model detailing:\n– What you will be classifying (has to be multi-class classification!)\n– Where your data comes from (provide a link or methodology of getting the dataset). If you are using an existing dataset\, you have to add more images from online means (gather data from outside of kaggle\, whether it’s online images\, etc)\n– How you will train your AI model (what transformations on the data you will use\, and the flow of training)\n\nEnsure the google doc is public access and that it is in your homework folder. \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-5/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260202T190000 DTEND;TZID=America/Los_Angeles:20260202T200000 DTSTAMP:20260721T101355 CREATED:20260203T041351Z LAST-MODIFIED:20260203T041351Z UID:32359-1770058800-1770062400@www.ayclogic.com SUMMARY:7 PM – AI/ML – Darin DESCRIPTION:Today’s Activities:\n\nRevisited homework on using different models for training and analyzed their performance.\nLearnt about virtual environments and dependencies (your “copy” of the computer)\nSetup github and demonstrated web app deployment (now you can ask your friends to check out your ai models haha)\n\nHomework:\n\nCopy the Jan26_CatVSDog.ipynb into Feb2_PetBreed.ipynb\nMODIFY the labeling function such that it labels by the dog BREED.\nYou do not return “dog” vs “cat” but rather you return “Siamese”\, “Abyssinian”\, etc \nHint: You need to only keep the string WITHOUT numbers.\nRun training again after modifying\nThis should result in MULTI-class classification\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-4/ CATEGORIES:AI/ML,Python Class END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260126T190000 DTEND;TZID=America/Los_Angeles:20260126T200000 DTSTAMP:20260721T101355 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 ” 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Los_Angeles:20260119T190000 DTEND;TZID=America/Los_Angeles:20260119T200000 DTSTAMP:20260721T101355 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 END:VCALENDAR