Log Loss | Logistic Regression | Derivative Calculation | Sigmoid Function Derivative | Explained

📘 Notes:- https://robosathi.com/docs/machine_learning/supervised/logistic_regression/log-loss/
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🎥 Related Video: Matrix Calculus :- https://youtu.be/41B-VWrwxKY
Calculus Fundamentals :- https://youtu.be/qpAieJgrcR8
👉In this video we will understand how Log Loss works in binary classification. We’ll break down derivatives, sigmoid function, and cost function step by step for clear understanding.

🎯 Learning Objectives
✅ Understand Binary Classification and its challenges
✅ Learn what Log Loss is
✅ Derive the Cost Function and understand its components
✅ Calculate gradients for predictions and cost function
✅ Understand Sigmoid Derivative

👉 Maths for ML Playlist:
https://www.youtube.com/playlist?list=PLnpa6KP2ZQxePOg6k6vAkcg5Y50EAZds9

🎥 Gradient Descent- https://youtu.be/I23bqvGWPEM

🕔 Time Stamp 🕘
00:00:00 - 00:00:27 Introduction
00:00:28 - 00:02:30 Binary Classification
00:02:31 - 00:06:50 What is Log Loss?
00:06:51 - 00:10:02 Cost Function
00:10:03 - 00:11:25 Gradient Descent
00:11:26 - 00:13:28 Gradient Calculation
00:13:29 - 00:15:27 Cost Function Derivative
00:15:28 - 00:16:39 Prediction Derivative
00:16:40 - 00:19:43 Sigmoid Derivative
00:19:44 - 00:20:43 Distance Derivative
00:20:44 - 00:21:37 Gradient Combination
00:21:38 - 00:22:51 Cost Function Derivative
00:22:52 - 00:27:00 Why MSE not used as Loss Function
00:27:01 - 00:27:47 What's Next? 🤔
#ml #ai #binaryclassification #logloss #mse #costfunction #gradient

Видео Log Loss | Logistic Regression | Derivative Calculation | Sigmoid Function Derivative | Explained канала RoboSathi

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