Machine Learning FOR BEGINNERS - Binary Cross Entropy Loss Error Function

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! 🤩

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: https://morioh.com/p/827bccc304c8

⭐ Clone the complete code via Wayscript ⭐ https://wayscript.com/script/Vr5xqHfR

⭐ Clone the Perceptron code from the previous episode⭐ https://wayscript.com/script/VlHE0uL8

⏰ 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

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