Videos

Intro to Machine Learning (ML Zero to Hero – Part 1)



TensorFlow

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself. But what does ML actually look like? In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney@) walks through a basic Hello World example of building an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning β†’ https://goo.gle/2Zp2ZF3

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.

Watch more Coding TensorFlow β†’ https://bit.ly/Coding-TensorFlow
Subscribe to the TensorFlow channel β†’ http://bit.ly/2ZtOqA3

Source

Similar Posts

42 thoughts on “Intro to Machine Learning (ML Zero to Hero – Part 1)
  1. Subscribed after watching this. Love the way you explain. You explain the concept very clearly and also you add a little bit of the code which gives me a great preparation for the coding application. Keep up the good work Lawrence

  2. Great video. You mention the small error is due to uncertainty due to the low sample size, is it not possible that the model simply descended to a not quite accurate relationship? Granted the cause would still be low sampling but the main question is if the error is explicitly programmed to reflect uncertainty because the input could still be 19 and be labeled uncertain.

  3. Wow amazing explanation .Well I'm a noob in this technology things , but I want to learn hacking , how can I learn it .???? Can anyone suggest anything for me to start learning and understanding all these computer language and all those programming 😁

  4. Here's a dumb question from a guy who doesn't know anything (that I couldn't find a clear and obvious answer for): Does Tensorflow send your data away for remote processing, or is it / can it all be done locally to the machine where the code is written?

  5. I have a question on something I don't understand: Dr. Moroney said that prediction is not perfect because the computer is trained for 6 values that form a straight line, but outside those 6 may be not straight (although it is highly probable that they are straight). I don't get this point: since it is a NN with only one neuron, so it has to be a straight line the prediction (it should be like a linear regression). Am I correct? Or did I interpret something wrong?

  6. I must suck at image recognition, because I couldn't see any "scissors" from the first 3 images πŸ™ˆ

    Thumb sticking out? Thats not scissors, thats counting to 3 on your hand in German

  7. i'd program the bone rig patterns, that's how i'd sort the ro sham bo.
    by applying the rig patterns to catagories, and then spawning the one that applies. not hard to do that,

    the computer could then rand choose based on it's available choices, that's AI for dummies, and i'm a dummy.

  8. What I really want to know is what exactly are the things happening with that single neuron/layer… Forgive my poor understanding but this video is just like saying, "if you put a dough in the oven and turn it on, it will come out as bread"… The contents of the dough was explained but I would like to know the mechanism in the oven and how it turned the dough into bread.

  9. As someone who had just begun self learning programming, this explanation about machine learning is very clear and understandable. Thank you!

  10. This is awesome mike the best explanations i have ever made on Machine Learning and i got a feel and beauty of nerual network when i heard your class , great job , keep posting like these cheers

  11. my procrastination has transcended to new levels
    I am watching this instead of studying for my 2 finals or working on my 4 remaining projects
    with less than 2 weeks left to finish all of those things lol

  12. In traditional programming, we infer answers after rules act on data, but in ML, we infer rules after answers act on data. Got that really straight.❀️❀️❀️❀️

  13. I really loved the videos then liked all before watching cos i am sure I will watch all πŸ˜€ inshaallah πŸ˜€ Thanks for yoru effort!

  14. Small python snippets always look great and then you try them and just see bs errors:
    > pip install keras
    > import keras
    > Result: AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'

    The rate at which python packages fail or become obsolete is scary.

  15. If only Isaac Asimov was still alive to see what marvel of information sharing we have achieved , … and it’s free

  16. You know those videos that you start watching and then get glued to them… πŸ˜€ Well done Laurence, in the first few seconds I wouldn't have bet on watching it

  17. I don't know about the basis of programming so this unfortunately made no sense to me. Also I didn't understand the math in the end, 19, what are we even talking about. x) I might have to go to even more basic level than these videos, heh. πŸ™‚

  18. stupid question: where do i tell the thing that the relation between x and y is a math problem? how does it know that it has to calculate something and not come up with something else?

Comments are closed.

WP2Social Auto Publish Powered By : XYZScripts.com