Video Tutorials¶
Mealpy Tutorial Full¶
Part 1: Youtube P1, Part 2: Youtube P1
Please read the description in the video for timestamp notes
Or watch the Full Video with timestamp notes below:
0:00 - Intro
0:19 - Download and install Miniconda on Windows 11
1:22 - Create a new environment using Miniconda
2:32 - Install Mealpy
5:08 - Pycharm and set environment on it
9:22 - Introducing the structure of Mealpy library
10:16 - The Optimizer class
10:50 - The Problem class
11:44 - The Termination class
15:10 - The History class (How to draw figures)
16:37 - How to import the mealpy library (Optimizer class)
18:32 - Define a problem dictionary (problem instance of Problem class)
19:32 - Define objective-function
21:18 - Problem definition (Find minimum of Fx function)
23:10 - How to call an optimizer to solve optimization problem
25:38 - The Problem class
26:23 - Sequential, Thread and Process training mode setting
28:23 - Explaining the current best and global best (training output)
29:18 - How to get final fitness and final position (solution)
30:38 - The structure of the "solution" attribute in Optimizer class
33:48 - Other ways to pass Lowerbound and Upperbound in problem dictionary
36:05 - How to import and define the Termination object
43:08 - Time-bound termination object
45:16 - Early Stopping termination object
47:18 - How to use Sequential/MultiThreading/MultiProcessing training mode
51:58 - Fix error with MultiProcessing training mode
55:54 - How to deal with Multi-objective Optimization Problem
1:05:09 - How to deal with Constrained Optimization Problem
1:11:46 - How to draw some important figures using History object
1:23:15 - How to use Mealpy to optimize hyper-parameters of a model
1:26:15 - Using Mealpy to optimization hyper-parameters of a traditional SVM classification
1:30:18 - Brute force method for tunning hyper-parameters
1:36:18 - GridSearchCV method for tunning hyper-parameters
1:39:28 - Metaheuristic Algorithm method for tunning hyper-parameters
Mealpy + Neural Network¶
Gradient Descent Replacement¶
Metaheuristic Algorithm in general can replace the Gradient Descent optimization to train the neural network. Youtube Link
For Time-Series Problem:
For Classification Problem:
Optimize ANN Hyper-parameter¶
Metaheuristic Algorithm also can optimize Hyper-parameter of Neural Network. Youtube Link
Link Code for Classification problem.