91 thoughts on “Teachable Machine 1: Image Classification

  1. Thanks for the new video! Always appreciate the interesting and inspiring content from here ☺. It certainly helps to eliminate the fear barrier to try new things!

  2. Interesting!
    Is that a neural network? I’ve learn abt image processing using matlab for neural networks. The concept is similar.

  3. Sir I love you. My question is "can i import the machine learning model into a python/C++ sketch in tensorflow"???
    I have just started working on Self Driving Car as my "Final Year Project" and i think it can help me a lot!!

  4. Excellent Work, I compleatly enjoyed it!, See this New Album 'Monish Jasbird – Death Blow', channel link www.youtube.com/channel/UCv_x5rlxirO-WKjLIyk6okQ?sub_confirmation=1 , you might like 🙂

  5. This whole thing is implemented in JavaScript? That's really cool! Although for bigger datasets, you probably want to train with a pytorch or tensorflow script to take advantage of your GPU

  6. For the default value of train you can check if the percentage of how sure the model is below a certain threshold then make the label waiting and only take model guess as an answer only if it passes that threshold

  7. if( mostWantedFaces.includes(label) ) {
    navigator.geolocation.getCurrentPosition(function(position) {
    send_2_FBI(position.coords.latitude, position.coords.longitude);

  8. You need to train it for an idle stance. just train it to recognize just "Dan" so every other mode will just pop up when it should rather then it detects the most "Dan" in any of the other features.

  9. Feeling excited to create something. Your truly amazing teacher Sir, Dan I can tell even beginner will enjoy your video.

  10. Awesome video! Btw, I was using an RNN on a vector of sequential data for classification, but RNNs are slow. Any suggestions that might work well in less time? Thanks! 🙂

  11. I don’t know how to code and I want to be a computer engineer in the future, I’m now an 11th grade student, I’m scared, very scared…….. bbrrrRrrrRrrr

  12. Love the videos, you're the best ever!!! So awesome literally watched them all over the last month. #suggestion Could you please please please do a video on OCR, like if you input an image of a receipt, the output would be text. Wondering if Teachable Machine can handle this, going to try now! Would love to hear your thoughts

  13. when I do It it comes up with a type error: Failed to fetch
    The webcam still works but the console doesn't come up with the objects.
    Can anyone help me?

  14. Getting this happy from very simple things? You're a very luck man, Dan, you have solved the mystery of the universe.

  15. Wow. I'm a guy who works at a cyber security company. I wish I could give it tonnes of logs that are threat and not threat and I'll just send logs to it. 😀

  16. Interesting…but what about a field that I never saw in your videos…maybe NLP, Personalized Entity Recognition, or other kind of this NLP stuff. Great video.

  17. You are a train. No doubt about it

    No. Sometimes you are a ukulele

    I typed both of the above comments even before 15:40. I literally cracked open when seeing that XDD

  18. Does teachable machine really trains the model on the browser or does it upload the dataset to the cloud and send results back to the browser ? I am curios because if I have a device with lower hardware configuration will it still work ?

  19. Hi,
    When I run the example from p5js web editor: https://editor.p5js.org/codingtrain/sketches/PoZXqbu4v
    I get this error Failed to link vertex and fragment shaders.

  20. I wish i was 13 again and had you as a teacher in programming BASIC and ASSEMBLER!! I submit to your channel from this one video!

  21. How to classify a 'nothing' image?
    If I set in example 2 images, the classifier gives me always 2 options, but using the web camera you watch also other objects not in the list of images classified.

  22. He did BIG MISTAKE in 13:44 he said "object, object, object, object, object" instead "object, object, object, object" like in screen.

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