LearnCode.academy
If you know nothing about how a neural network works, this is the video for you! I’ve worked for weeks to find ways to explain this in a way that is easy to understand for beginners.
Past Videos:
Intro to Machine Learning with Javascript:
https://www.youtube.com/watch?v=9Hz3P1VgLz4&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=2&t=0s
Machine Learning 2 – Building a Recommendation Engine:
https://www.youtube.com/watch?v=lvzekeBQsSo&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=3&t=0s
Machine learning and neural networks are awesome. This video provides beginners with an easy tutorial explaining how a neural network works – what math is involved, and a step by step explanation of how the data moves through the network.
The example used will be a feed forward neural network with back propagation. It explains the difference between linear and non linear data, the importance of the activation function, learning rate, and momentum configurations.
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Learning Web Development? Watch the FREE COURSE:
“Web Development for Absolute Beginners”!
https://www.youtube.com/watch?v=gQojMIhELvM
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Source
crystal clear explanation. thanks
Great overview and good vibes!
I wish I could like 2 times
where do you apply the calculated change? It didn't affect the weights and biases at all in the diagram.
-_- This is not for beginners..!
I think maybe you could give more detailed calculations and visualize it from each iterations so that viewers can understand how the calculation works
Im,seven,you,think,I,know,what,whosits,and,whatsits,are,
What calculations did you use to find the activation function and the output nodes. I think I missed something or didn't understand how you got those numbers by the end of the first iteration.
play this at 0.25% speed!!
Finally someone, who knows which aspects of ML are most unclear for beginners and explains them in a very simple way!
Thank you!
bad example !
I came to this video first. Watched it twice. Was still confused. Went on to watch 5 different other vids on NN’s and 2 vids on CNN’s. Revisited this video. Now it makes sense
once I built a trained NN with my sample data, how can I use it with new inputs to get the outputs?
thanks
Im 12 btw
Hey what does the * value refer to in your change formula
excellent video… but you came out short with the explanation of the calcs of backprop…
no idea what delta is…. no idea what pastChange is… and you don't have to change just the values and the weight… you have to change the biases… so…………. i hope this is in the next video…
really good so far
too number-filled explanation. The graphs/activation stuff you showed didn't actually help with the explanation. Why would I use sigmoid or tanh…? Felt like you skipped over backprop with bare mentions. I think your explanation made sense, it just hard to visualize for a beginner. Maybe you need code.
Hello, everybody. I'm Thanapol from Chulalongkhon University ,Thailand
Ayeee, thank you for scrunching all this heavy content information in a way that makes sense. Props to you!
Great video! Thanks for sharing!
Ok. So after you apply the activation function to the sum. What then do you do to it? The arrows on the right arnt explained.
Thanks for the equations by the way, huge help!
what makes people think rocket science is so hard
If you watch this video a few times, it'll help you!!
Lmao….
What are you adding some slight reverb to your vocal track? The acoustics sound great like you're on stage, not in a bedroom in front of a computer lol.
Great intro thanks!
Very math-free explanation and it is very helpful to pick up the basics. Nice job!
how comes i ended up here LOL
Another issue with lower learning rates is local minima
Crystal clear
This is amazing!