Udemy – Convolutional Neural Networks for Medical Images Diagnosis 2020-6

Convolutional Neural Networks for Medical Images Diagnosis is a training course on convolutional or convolutional neural networks and their application in medical imaging, published by Udemy Academy. This educational course is completely project-oriented and practical and contains a series of scattered topics, among the most important of which are convolutional or convolutional neural networks (CNN), deep learning, medical imaging, and transfer learning. , detailed illustration and drawing of convolutional neural networks, VGG deep neural network, residual neural network (ResNet), Python programming language, Keras library, etc. This training course will provide you with several examples and through these examples will teach you the working process of layers in CNN.

Practicing convolutional neural networks and evaluating its different parts, improving the overall performance and speed of CNN, illustrating neural network layers, final implementation of neural models, etc. are among the most important topics that will be taught in this training course. became.

What you will learn in Convolutional Neural Networks for Medical Images Diagnosis:

• Convolutional or convolutional neural networks (CNN)
• Deep learning and its application in medical imaging
• Transfer learning
• Illustration and accurate drawing of convolutional neural networks
• Improved performance and overall speed of CNN
• Visualization of neural network layers
• Final implementation of neural models
• And …

Course specifications

Publisher: Udemy
Instructors: Hussein Samma
Language: English
Level: Intermediate
Number of Lessons: 29
Duration: 1 hours and 29 minutes

Course topics

Convolutional Neural Networks for Medical Images Diagnosis Prerequisites

Have the basic knowledge about CNN
Familiar with Python programming
Spyder editor with Python 3.7

Pictures

Convolutional Neural Networks for Medical Images Diagnosis introduction video

Installation guide

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English subtitle

Quality: 720p

Download link

Download Course – 635 MB

Size

635 MB