08.20-09.00

ลงทะเบียน

9.00-10.30 น.

Module1: Welcome to the course

Module2: Get to know about Data

Module3: Introduction to Machine Learning 101 (ML Types)

Module4: Classification and Regression Models

10.30-10.45

Break

10.45-12.00

Module5: Introduction to Artificial Neural Network (ANN)

·       The Neuron

·       The Activation Function

·       How do Neural Networks work? (example)

·       How do Neural Networks learn?

·       Gradient Descent

·       Stochastic Gradient Descent

·       Back propagation Network optimization

12.00-13.00

พักรับประทานอาหาร

13.00-15.00

 Module6: Introduction to Convolutional Neural Network (CNN)

·       What are Convolutional Neural Networks?

·       Convolutional Operation

·       Pooling Operation

·       Flattening Operation

·       Full Connection

·       Summary

15.00 – 15.15

Break

15.15 – 16.45

Module7: Introduction to Recurrent Neural Network (RNN)

·       The idea behind Recurrent Neural Networks

·       The Vanishing Gradient Problem for RNNs

·       Long-Short Term Memory

·       Practical Intuition

Module8: Wrap Up all Machine Learning and Deep Learning

16.45-17.00

·       สรุปเนื้อหาการอบรม

·       มอบประกาศนียบัตร