Full Description
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
Contents
Section I Deep Learning Basics and Mathematical Background
1. Introduction to Deep Learning
2. Probability and information Theory
3. Deep Learning Basics
4. Deep Architectures
5. Deep Auto-Encoders
6. Multilayer Perceptron
7. Artificial Neural Network
8. Deep Neural Network
9. Deep Belief Network
10. Recurrent Neural Networks
11. Convolutional Neural Networks
12. Restricted Boltzmann Machines
Section II Deep Learning in Data Science
13. Data Analytics Basics
14. Enterprise Data Science
15. Predictive Analysis
16. Scalability of deep learning methods
17. Statistical learning for mining and analysis of big data
18. Computational Intelligence Methodology for Data Science
19. Optimization for deep learning (e.g. model structure optimization, large-scale optimization, hyper-parameter optimization, etc)
20. Feature selection using deep learning
21. Novel methodologies using deep learning for classification, detection and segmentation
Section III Deep Learning in Engineering Applications
22. Deep Learning for Pattern Recognition
23. Deep Learning for Biomedical Engineering
24. Deep Learning for Image Processing
25. Deep Learning for Image Classification
26. Deep Learning for Medical Image Recognition
27. Deep learning for Remote Sensing image processing
28. Deep Learning for Image and Video Retrieval
29. Deep Learning for Visual Saliency
30. Deep Learning for Visual Understanding
31. Deep Learning for Visual Tracking
32. Deep Learning for Object Segmentation and Shape Models
33. Deep Learning for Object Detection and Recognition
34. Deep Learning for Human Actions Recognition
35. Deep Learning for Facial Recognition
36. Deep Learning for Scene Understanding
37. Deep Learning for Internet of Things
38. Deep Learning for Big Data Analytics
39. Deep Learning for Clinical and Health Informatics
40. Deep Learning foe Sentiment Analysis
