In this video, we will talk about Gradient Descent and how we can use it to update the weights and bias of our AI model. We will learn how to minimize the average loss of our model, and get a warm introduction to "epochs" and "learning rate"!
We will of course also see a working example of the math behind Gradient Descent, and learn how to implement it with code by using our superior Python skills!
Before we dive in, make sure you are proficient with the previous topics of my AI series Perceptron, Weights, Input, Weighted Sum, Target, Prediction, Activation Function, Loss Function & CrossEntropy Loss.
If you are new to these concepts, please watch my other tutorials first (links below):

✋ HAVE YOU WATCHED? ✋

⭐ My CrossEntropy Loss Tutorial ⭐:
   • Cross Entropy Loss Error Function  M...  

⭐ My Perceptron Tutorial ⭐
   • Perceptron Algorithm with Code Exampl...  

⭐ My Introduction to ML Tutorial ⭐
   • Machine Learning FOR BEGINNERS  Supe...  

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⭐ time stamps ⭐
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00:00 what is gradient descent?
00:37 gradient descent vs perception
01:04 sigmoid activation function
01:45 bias and threshold
02:06 weighted sum working example
02:37 sigmoid working example
03:03 loss function working example
03:32 how to update weights
04:17 what is learn rate?
05:06 how to update bias
05:37 gradient descent working example
07:13 what is epoch?
07:38 average loss per epoch
08:37 gradient descent code example
12:13 thank you for watching! stay in touch!

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⭐⭐⭐ Get Starter And Complete Code (UPDATED FEB 2023) ⭐⭐⭐
https://github.com/MariyaSha/Gradient...
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All the beautiful icons used in the video are by: freepik.com
Thank you!