04 - Introduction to Optimisation and the Gradient Descent Algorithm

04 - Introduction to Optimisation and the Gradient Descent Algorithm: 00:00:00 __ 001 What s Coming Up 00:02:42 __ 002 How a Machine Learns 00:08:08 __ 003 Introduction to Cost Functions 00:15:36 __ 004 LaTeX Markdown and Generating Data with Numpy 00:31:02 __ 005 Understanding the Power Rule & Creating Charts with Subplots 00:45:53 __ 006 Python - Loops and the Gradient Descent Algorithm 01:22:55 __ 007 Python - Advanced Functions and the Pitfalls of Optimisation (Part 1) 02:00:30 __ 008 Python - Tuples and the Pitfalls of Optimisation (Part 2) 02:30:35 __ 009 Understanding the Learning Rate 03:00:15 __ 010 How to Create 3-Dimensional Charts 03:24:54 __ 011 Understanding Partial Derivatives and How to use SymPy 03:43:16 __ 012 Implementing Batch Gradient Descent with SymPy 03:55:41 __ 013 Python - Loops and Performance Considerations 04:11:36 __ 014 Reshaping and Slicing N-Dimensional Arrays 04:30:50 __ 015 Concatenating Numpy Arrays 04:38:29 __ 016 Introduction to the Mean Squared Error (MSE) 04:49:19 __ 017 Transposing and Reshaping Arrays 05:02:10 __ 018 Implementing a MSE Cost Function 05:14:26 __ 019 Understanding Nested Loops and Plotting the MSE Function (Part 1) 05:26:15 __ 020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2) 05:42:33 __ 021 Running Gradient Descent with a MSE Cost Function 06:02:26 __ 022 Visualising the Optimisation on a 3D Surface