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Deep learning Calculus - Data Science - Machine Learning AI
Introduction to Calculus
Understanding the Function (3:27)
Calculus Basics (4:20)
Finding a Derivative (7:16)
Derivatives using Delta Method (11:13)
Product Rule for Differentiation (8:28)
Chain Rule (3:38)
Applying all the basics (3:27)
End of Section 1 (0:37)
Multi-Variate Calculus
Multi Variate Calculus (3:59)
Differentiate With respect to anything (5:02)
Jacobians (4:09)
Hessian (2:34)
Chain Rule on Multi Variate
Chain Rule on Multi Variate (4:07)
Chain Rule on Multi Variate - more functions (5:05)
Neural Networks
Neural Networks - Intro (9:15)
Bias in Neural Networks (2:58)
Neural Networks Part 2 (4:49)
Calculus in Action - Neural Networks (8:10)
Intuition of Sigmoid Function (5:48)
Manual Fitting of Data (8:15)
Loss Function (4:25)
How to Update Parameters (9:20)
Compute Partial Derivative (8:07)
Exercise to compute Partial derivative of parameter - bias (2:54)
Program overview (3:30)
Program in Python (12:09)
Taylor Series of Approximation
Taylor Series of Approximation (0:37)
Concept of Approximation (4:38)
Taylor Series - Intuition (4:09)
Taylor Series Detailled (10:36)
Taylor Series Derivation (10:43)
Taylor Series Derivation Part 2 (6:40)
Taylor Series - More (10:25)
Optimization Methods - Newton Raphson - Gradient Descent
Newton Raphson Method (10:32)
Newton Raphson Method in Python (4:23)
Gradient Descent (5:50)
Linear Regression
Linear Regression (7:07)
Linear Regression in Python (10:52)
Evaluation of Model - RMSE and R2 Score (4:26)
Implementation using Scikit Library (3:25)
Solution for Exercise
Solution exercise 1 (2:51)
Solution exercise 2 (4:55)
Solution exercise 3 (1:59)
Solution exercise 4 (4:21)
Solution exercise 5 (1:06)
Solution exercise 6 (3:11)
Solution exercise 7 (1:46)
Solution exercise 8 (1:00)
Hessian
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