Withdrawal method: Baidu.com
Total [20] sections
Availability of courseware: Yes

You will gain

Understanding the basics of introductory OpenCV image processing

Understanding Machine Learning Fundamentals

Understanding Deep Learning Foundations

population (esp. of a group of people)

Engineers who want to transition to AI and are interested in the computer vision direction.
Courses

This course is a foundational course for vision application engineers that complements the introductory OpenCV image processing fundamentals, machine learning fundamentals, and deep learning fundamentals.


Course Catalog

Chapter one:
Chapter 1: Machine Learning Fundamentals
Overall introduction to the machine learning program 04:35
Keras implementation of linear regression 01-Data Division 08:21
Keras Implementation of Linear Regression 02 - Model Building 05:16
Keras Implementation of Linear Regression 03 - Loss Functions, Gradient Descent, and Evaluation Metrics 05:25
Keras Implementation of Linear Regression 04 - Model Setup, Model Validation, Model Prediction 11:54
Sklearn implementation of linear regression 07:31
Example of polynomial fitting 19:38
Underfitting and overfitting 11:17
A sklearn implementation of logistic regression 15:02

Chapter II:
Chapter 2: Foundations of Deep Learning
Fully connected neural networks 09:47
Keras implementation of fully connected neural networks 11:35
Reasons why fully connected neural networks do not converge 10:03
Feature scaling 02:17
Weight initialization 04:40
Activation function 10:34
Batch Normalization 06:37
Neural Network Overfitting Treatment 01-Increasing Data Volume & Reducing Model Complexity 08:46
Neural Network Overfitting Treatment 02 - Adding Regular Terms and Tensorboard Visualization 11:40
Neural Network Overfitting Handling Methods 03-Advance Termination 03:07
Neural Network Overfitting Treatment 04-Dropout 06:02

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