Image Classification with Convolutional Neural Network (CNN) model with Dropout Layer
Convolutional Neural Network (CNN) is a very popular Deep Learning algorithm for image classification. It typically consists of Convolution layers with RELU activation function and max pooling layers for feature extraction. After the feature extraction, the final feature will be flattened for normal classification. There are many resources on the internet explaining the theory of CNN, so we will not elaborate here.
In this bitesize video, we will demonstrate how to perform CNN on the CIFAR dataset, evaluate the performance of the model and improve the model using a dropout layer.
Python code used for the demo in this video can be downloaded below
https://github.com/tertiarycourses/Te...
1. Recommended WSQ courses on Machine Learning and Deep Learning
NICF - Basic Machine Learning with Scikit-Learn Course
https://www.tertiarycourses.com.sg/ws...
NICF – Basic Deep Learning with Tensorflow Keras
https://www.tertiarycourses.com.sg/ws...
NICF – Pattern Recognition with Deep Learning
https://www.tertiarycourses.com.sg/ws...
2. Recommended Computer Vision Courses
https://www.tertiarycourses.com.sg/de...
https://www.tertiarycourses.com.my/de...
https://www.tertiarycourses.com.gh/de...
Other training courses from Tertiary Courses
https://www.tertiarycourses.com.sg/
https://www.tertiarycourses.com.my/
https://www.tertiarycourses.com.gh/