DeapSECURE module 4: Deep Learning (Neural Network): Credits for materials used

Image credits

• SherLock data table:

  “The Smartphone Agents,” Sherlock: 2016. https://web.archive.org/web/20190829054234/http://bigdata.ise.bgu.ac.il/sherlock/#/dataset#dataset5

• Image of network of neurons, posted at Max Pixel https://web.archive.org/web/20190317134655/https://www.maxpixel.net/Neurons-Brain-Structure-Network-Brain-Cells-Brain-440660 This image is in public domain. license of Creative Commons Zero CC0. Use this image with referral.

• Image of a neuron, by Wikipedia user BruceBlaus

  https://en.wikipedia.org/wiki/Neuron#/media/File:Blausen_0657_MultipolarNeuron.png Author is BruceBlaus. This work is free and may use by anyone. CC BY 3.0 https://creativecommons.org/licenses/by/3.0/

• Gradient Descent, by Wikipedia user Jacopo Bertolotti

  https://commons.wikimedia.org/wiki/File:Gradient_descent.gif Author is Jacopo Bertolotti. This work is available under Creative Commons CC0 1.0 Universal Public Domain Dedication..

• Color image of a fully-connected neural network with one hidden layer, by Wikimedia user Glosser.ca

  https://en.wikipedia.org/wiki/Artificial_neural_network#/media/File:Colored_neural_network.svg https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg CC BY-SA 3.0

  Modified by WP to match the fill color to conventions used by Asimov Institute’s neural network zoo.

• Sequential NN model and Functional NN Model diagrams:

  fchollet. “The Functional API.” Keras: 2023. https://keras.io/guides/functional_api/

• Dropout illustration

  Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.

• Resnet introduction

  He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

• Parameters in Notable AI Systems

  “Data Page: Parameters in notable artificial intelligence systems”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska, and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from https://ourworldindata.org/grapher/artificial-intelligence-parameter-count [online resource] Creative Commons BY license

• Transformer NN Architecture Diagram

  A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” CoRR, abs/1706.03762.