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In this lesson we will simplify the binary Log Loss/Cross Entropy Error Function and break it down to the very basic details. I'll show you all kinds of illustrated and fun examples and even implement everything with code! 🤩 Before you watch this tutorial 🛑please make sure you watch the first episode in this series🛑 where we discuss the Perceptron model: • Perceptron Algorithm with Code Exampl... ⭐ Clone the complete code via Wayscript ⭐ https://wayscript.com/script/Vr5xqHfR ⭐ Clone the Perceptron code from the previous episode⭐ https://wayscript.com/script/VlHE0uL8 🤖 Watch my Introduction to AI & Machine Learning 🤖 • Machine Learning FOR BEGINNERS - Supe... ➰ Watch my Python "For" loop tutorial ➰ • Python For Loops - Programming for Be... **************************** ⏰ TIMESTAMPS ⏰ **************************** 00:00 - Perceptron Recap 00:33 - Target vs Prediction 01:16 - Error Function 01:52 - Cross Entropy Loss / Log Loss Data Preprocessing 03:19 - Cross Entropy Loss on a single data entry 04:31 - Cross Entropy Loss Properties 05:19 - Cross Entropy Loss on all data entries 06:17 - Coding Cross Entropy Loss with Python **************************** Sorry guys, no subtitles this time as they would cover substantial parts of the graphics 😥 The beautiful icons used in this video are by: https://www.flaticon.com https://www.freepik.com Thank you so much for watching! ❤