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X-WR-CALNAME:American Young Coder
X-ORIGINAL-URL:https://www.ayclogic.com
X-WR-CALDESC:Events for American Young Coder
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TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
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TZNAME:PDT
DTSTART:20260308T100000
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DTSTART:20261101T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260415T190000
DTEND;TZID=America/Los_Angeles:20260415T200000
DTSTAMP:20260420T140504
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:20260420T140504
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:20260420T140504
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/<your username>/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:20260420T140504
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:20260420T140504
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/<your username>/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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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:20260420T140504
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260119T190000
DTEND;TZID=America/Los_Angeles:20260119T200000
DTSTAMP:20260420T140504
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:20260112T190000
DTEND;TZID=America/Los_Angeles:20260112T200000
DTSTAMP:20260420T140504
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:20260105T190000
DTEND;TZID=America/Los_Angeles:20260105T200000
DTSTAMP:20260420T140504
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
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END:VCALENDAR