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Covers: theory of Deep Learning
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Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

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Getting Started With Mathematics Of Deep Learning

Contributors
Total time needed: ~7 hours
Objectives
This recipe is your starting point to understand why and how deep learning works.
Potential Use Cases
Image processing and computer vision, Natural language processing
Who is This For ?
BEGINNERData Scientists, Machine Learning Engineers
Click on each of the following annotated items to see details.
Resources
VIDEO 1. Introduction: Pytorch and Linear Algebra
  • Why does deep learning work?
  • Why PyTorch?
  • Why linear algebra?
20 minutes
RECIPE 2. Foundations of Algebra for Deep Learning
2 hours
RECIPE 3. Deep Learning Model Training and Optimization
3 hours
RECIPE 4. Extracting Features using Convolution
3 hours
VIDEO 5. Short intro of Conv Nets
  • What is the convolution operation?
  • What are the filters inside the CNN?
5 minutes
PAPER 6. Deep learning with convolutional neural network in radiology
10 minutes

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