About
About the Website's Creation
- All Content on this website was made solely by the author (Haylam Yuen)
- All HTML, CSS and JavaScript was written solely by the author (Haylam Yuen), although gaining inspiration from online resources, most notably GeeksforGeeks and W3 Schools
- The design architecture was inspired by wireframes from https://dribbble.com/search/wireframe
- The Colour Scheme was created using https://coolors.co/generate
- The Logo and Favicon were created with the use of Design.com AI
- Some diagrams were created by the author (Haylam Yuen) using Canva, others created using ChatGPT Sora, and some borrowed from other websites (with appropriate referencing)
- This website was last updated on September 14, 2025
Using the Website
- All content and explantions from this website can be reused and copied for educational purposes
- Commercial use of the diagrams / explanations on this website is allowed, although with appropriate referencing and contacting the author (Haylam Yuen)
- The author (Haylam Yuen), is not legally responsible for any harm caused by this website, including but not limited to epilepsy seizures in some photosensitive viewers and minor PTSD.
Bibliography
3Blue1Brown. (2017). Essence of calculus [Video playlist]. YouTube https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
BotPenguin. (n.d.). The vanishing gradient problem. BotPenguin. https://botpenguin.com/glossary/vanishing-gradient-problem
BotPenguin. (n.d.). What is softmax? BotPenguin. https://botpenguin.com/glossary/softmax-function
Calc Workshop. (n.d.). Chain rule tutorial. Calc Workshop. https://calcworkshop.com/derivatives/chain-rule/
cmdlinetips. (2021). Linear separability in machine learning. cmdlinetips. https://cmdlinetips.com/category/data-science/
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Jayesh Bapu Ahire. (2020). The XOR problem in machine learning. https://dev.to/jbahire/demystifying-the-xor-problem-1blk
Kawala. (2015). Binary classification explained. https://www.researchgate.net/figure/This-illustration-present-a-binary-classification-that-is-performed-on-two-features-The_fig5_285653348
Khan Academy. (n.d.). Chain rule review. Khan Academy. https://www.khanacademy.org/math/ap-calculus-ab/ab-differentiation-2-new/ab-3-1a/a/chain-rule-review
Khan Academy. (n.d.). Introduction to partial derivatives. Khan Academy. https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/partial-derivatives/v/partial-derivatives-introduction
Khan Academy. (n.d.). Position vector valued functions. Khan Academy. https://www.khanacademy.org/math/ap-calculus-bc/bc-advanced-functions-new/bc-9-4/v/position-vector-valued-functions
Massachusetts Institute of Technology. (2020). Introduction to deep learning [Course materials]. MIT OpenCourseWare. https://ocw.mit.edu/courses/6-s191-introduction-to-deep-learning-january-iap-2020/
Massachusetts Institute of Technology. (n.d.). Learning in deep neural networks. MIT OpenCourseWare. https://ocw.mit.edu/courses/res-9-008-brain-and-cognitive-sciences-computational-tutorials/pages/4-learning-in-deep-neural-networks/
Massachusetts Institute of Technology. (n.d.). Structure of neural nets for deep learning. MIT OpenCourseWare. https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-26-structure-of-neural-nets-for-deep-learning/
Mazur, M. (2024, February 23). A step by step backpropagation example. Matt Mazur. https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
Neptune.ai. (2022, July 21). A comprehensive guide to the backpropagation algorithm in neural networks. Neptune.ai. https://neptune.ai/blog/backpropagation-algorithm-in-neural-networks-guide
Ng, A. (2022). Machine learning specialization [Online course]. Coursera. https://www.coursera.org/specializations/machine-learning-introduction
Ng, A. (n.d.). CS229: Machine learning. Stanford University. https://cs229.stanford.edu/
Nielsen, M. (n.d.). How the backpropagation algorithm works. Neural Networks and Deep Learning. http://neuralnetworksanddeeplearning.com/chap2.html
Nielsen, M. (n.d.). Neural networks and deep learning. http://neuralnetworksanddeeplearning.com/
Prince, S. J. D. (n.d.). Understanding deep learning. https://udlbook.github.io/udlbook/
Professor Dave Explains. (n.d.). Chain rule explanation [Video]. YouTube. https://www.youtube.com/watch?v=_x1nCg2LfuA
Professor Dave Explains. (n.d.). Calculus video series [Video playlist]. YouTube. https://www.youtube.com/@ProfessorDaveExplains
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Sora. (2025). AI-generated neuron diagram and cost function visualizations. OpenAI Sora.
Sora. (2025). Neural network architecture definitions and parallel processing concepts. OpenAI Sora.
Trefor Bazett. (n.d.). The gradient vector [Video]. YouTube. https://www.youtube.com/watch?v=QQPz3eXXgQI
Wikipedia. (2008). Newton's law of universal gravitation. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation
Wikipedia. (2025). Frank Rosenblatt. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Frank_Rosenblatt