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 & Cross-Entropy Loss. If you are 🆕 new 🆕 to these concepts, please watch my other tutorials first (links below): 🛑✋ HAVE YOU WATCHED? ✋🛑 ⭐ My Cross-Entropy Loss Tutorial ⭐: https://www.youtube.com/watch?v=EJRFP3WmS6Q ⭐ My Perceptron Tutorial ⭐ https://www.youtube.com/watch?v=-KLnurhX-Pg ⭐ My Introduction to ML Tutorial ⭐ https://www.youtube.com/watch?v=mMc_PIemSnU ***************************************** ⭐ time stamps ⭐ ***************************************** 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! ***************************************** ⭐⭐⭐ Get Starter And Complete Code (UPDATED FEB 2023) ⭐⭐⭐ https://github.com/MariyaSha/GradientDescent ***************************************** All the beautiful icons used in the video are by: freepik.com Thank you! 🤩