32 thoughts to “ml5.js: Pose Classification with PoseNet and ml5.neuralNetwork()”

  1. What's amazing is that the tools for doing stuff like this were only created a few years ago, and more amazing is The Coding Train covering it in super approachable and fun videos 😀

  2. Can you make a game for Jojo bizarre adventure poses.
    .
    The computer will print the name of random name of character in the anime.
    Then the player will guess the right pose of the character.

  3. SO Y MEANS ALL THE MOTORS IN THE ROBOT GO TO THEIR Y POSITION . SO COPY ALL THE LETTERS FROM THE POSES AND THE ROBOT DOES WHAT I DO .

  4. Hey sir MCA student can you help me.. to next study tell me suggest your programming language … my goal is Google sir

  5. 🦋🍇🐝🕳🔥💧🌸👂
    .,
    🌦 bRain-i-ng : https://www.geogebra.org/m/us6sf2ww

  6. So hyped that you used YMCA! I literally just used that with Teachable Machine to show my mom why I was so excited about machine learning when I was home for christmas.

  7. How is everyone finding the editing of this video compared to older videos? I'm finding it distracting and sometimes a bit jarring as it constantly speeds up with music and pops to new points. I kind of miss the slower pace. Coding Train still rocks my socks! <3

  8. this thing looks really cool – who wants to label pixel colour brightnesses when you can have the 2d localities, much better.

  9. I have a coding challenge for you.

    1. create a program that looks like stock on the market. Example: https://i.imgur.com/3RERXOf.png

    How I did it was rather easy, at the beginning of the "day" a random number between 0.46 and 0.54 was chosen, this was the bias for that day (effectively 50/50 given infinite time). Next another number was chosen at random but with 3 possibilities, between 200-300, 600-900, and 1800-2200 (32%/64%/4% respectively), which was the "volume." A loop was repeated "volume" times which would decide randomly between [0.0-1.0), and test this against the bias….if it were below the bias, it would reduce the "stock" by 0.01, if it were above, it would increase by 0.01. Each candle would be created just like any stock creates the candle, the open/close based on the starting/ending price of the stock for that day, and the high/low based on the high/low seen for the day. Overnight, there would be a 200 iterated loop of 50/50 to change the price for the next day's open (which gives the ability to create "gaps").

    2. build an indicator that takes the "ATR" (average true range) of this fake stock over time (14 bar moving)….this should be the easiest, I'll even give you the calculation:

    atr[x] = (max(high – low, abs(high – close[x-1]), abs(low – close[x-1])) + atr[x-1] * 13) / 14;

    3. Now the hard part, using only these data points (open, close, high, low, atr, volume), along with inputs (strike, days-to-expire/dte), construct an AI that will give you the optimal zero-sum cost of the call and put options. Meaning, a person who buys an option and another person who sells the same option, would both (on average) end up with no profit and no loss. We are entering an age where trades are free, so we do not have to worry about any other costs. So you don't have to look it up:

    Call option – buyer makes money if it expires above the strike price, seller gets money upfront but has to pay the difference at expiration if it is above strike price.
    Put option – same but if price is below strike instead

    Train the AI on many variations of fake stocks until it can be at or above 98% accurate…..I will donate $50 to your channel if you can send me the net and it is verified to backtest. I do NOT want black-scholes model, it isn't accurate enough. Feel free to add more indicators if you need to get the accuracy up, but please include them with the net.

    Thanks. 🙂

  10. Can you create, a login website with ml, so if you want to login you should to upload / Pict your face on login page..

    #CodingChallenge

    If you agree pls like this comment ^_^

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