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Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 4 - References/Further Reading

General Edition, Chapter 4 - References/Further Reading

 

  1. Wikipedia contributors. Arthur Samuel [Internet]. Wikipedia, The Free Encyclopedia. 2020 [cited 2021 Feb 19]. Available from: https://en.wikipedia.org/w/index.php?title=Arthur_Samuel&oldid=994321427
  2. Trappenberg TP. Introduction to Machine Learning. In: Fundamentals of Machine Learning. Oxford University Press; 2019. p. 1–14.
  3. Panesar A. Machine learning and AI for healthcare: Big data for improved health outcomes. 1st ed. APRESS; 2019.
  4. Bonaccorso G. Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models. Birmingham, England: Packt Publishing; 2018.
  5. Raschka S. Python Machine Learning. Birmingham, England: Packt Publishing; 2015.
  6. Little MA. Statistical Machine Learning. In: Machine Learning for Signal Processing. 2019. p. 149–186.
  7. Pant A. Introduction to Logistic Regression - towards data science [Internet]. Towards Data Science. 2019 [cited 2021 Feb 19]. Available from: http://towardsdatascience.com/introduction-to-logistic-regression-66248243c148
  8. Le J. A gentle introduction to neural networks for machine learning [Internet]. Codementor.io. Codementor; [cited 2021 Feb 19]. Available from: http://www.codementor.io/@james_aka_yale/a-gentle-introduction-to-neural-networks-for-machine-learning-hkijvz7lp
  9. Genetic Algorithms - Introduction [Internet]. Tutorialspoint.com. [cited 2021 Feb 19]. Available from: https://www.tutorialspoint.com/genetic_algorithms/genetic_algorithms_introduction.htm
  10. Learning Vector Quantization. In: Encyclopedia of Machine Learning and Data Mining. 2017. p. 737–737.
  11. 1.4. Support Vector Machines — scikit-learn 0.24.1 documentation [Internet]. Scikit-learn.org. [cited 2021 Feb 19]. Available from: http://scikit-learn.org/stable/modules/svm.html
  12. DataCamp Community. AdaBoost Classifier in Python [Internet]. Datacamp.com. [cited 2021 Feb 19]. Available from: http://www.datacamp.com/community/tutorials/adaboost-classifier-python
  13. Castrounis A. Artificial intelligence, deep learning, and neural networks, explained - KDnuggets [Internet]. Kdnuggets.com. [cited 2021 Feb 19]. Available from: https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html
  14. Tu A. NTUOSS-ImageRecognitionWorkshop [Internet]. [cited 2021 Feb 19]. Available from: https://github.com/anqitu/NTUOSS-ImageRecognitionWorkshop
  15. Sorokina K. Image Classification with Convolutional Neural Networks [Internet]. Medium. 2017 [cited 2021 Feb 19]. Available from: https://medium.com/@ksusorokina/image-classification-with-convolutional-neural-networks-496815db12a8
  16. Brownlee J. What is natural Language Processing? [Internet]. Machinelearningmastery.com. 2017 [cited 2021 Feb 19]. Available from: http://machinelearningmastery.com/natural-language-processing/
  17. Joshi AV. Open Source Machine Learning Libraries. In: Machine Learning and Artificial Intelligence. 2019. p. 221–232.
  18. Costa CD. Best python libraries for machine Learning and Deep Learning [Internet]. Towards Data Science. 2020 [cited 2021 Feb 19]. Available from: http://towardsdatascience.com/best-python-libraries-for-machine-learning-and-deep-learning-b0bd40c7e8c
  19. Introduction to TensorFlow - GeeksforGeeks [Internet]. Geeksforgeeks.org. 2017 [cited 2021 Feb 19]. Available from: http://www.geeksforgeeks.org/introduction-to-tensorflow/