Deep Learning Tutorial Part I: neural network theory

In recent years, remarkable progress has been made in deep learning. The medical image analysis community has taken notice of these pivotal developments as the number of publications is increasing exponentially.

This video is a deep learning introduction which is part of a deep learning tutorial. It answers questions like: What is deep learning? What is a neural network? Convolutional network explained? Are deep learning and machine learning different? Deep learning vs. machine learning? Deep learning vs. AI? AI vs. machine learning? What can deep learning do? What can neural networks do? What is CNN? What does CNN mean? What convolutional neural networks? What is DNN deep learning? What is RNN deep learning? What is DBN deep learning? What problems do the deep learning methods solve? How does CNN work? What are filters in CNN / What are convolutional network filters? How do filters in CNN work? Example for filters in CNN? What is pooling? What is the pooling layer? What are fully connected layers? ANN? How pooling works in CNN? How pooling layer works? Why pooling layer? What is a problem with pooling? How convolutional network visualization? Examples for CNN are provided. Examples for deep learning are provided.

The deep learning basics are explained.
Deep learning models brain cell communication. CNN, DNN, RNN and DBN are methods of deep learning. Deep learning is part of machine learning. Machine learning is part of the concept of AI. CNN means convolutional neural networks. CNN is a deep learning model. CNN works with images and image analysis. Examples for CNN are image classification, image segmentation / object detection with U-Net or YOLO and medical document translation. These are examples of CNN in medicine. CNN contains convolutional layers using filters and pooling, and fully connected layers (ANN). Each filter filters for different features, e.g. horizontal lines or round shapes. In the pooling layer, the following distinction can be made: max pooling and average pooling. The pooling layer works like a compressor. Pooling reduces the size of an image.

Chapters
0:00 Introduction
0:30 Deep learning and brain behaviour
2:00 AI vs. machine learning, machine learning vs. deep learning, deep learning vs. AI
2:50 Deep learning models
3:10 CNN – Convolutional Neural Network
3:40 Examples of CNN
5:20 Relationship between AI and doctors
5:50 CNN vs. other neural networks
6: 37 Filters in CNN
8:00 Pooling layer
9:32 Outlook: Implementation

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