Evolution of Neural Networks: Zero to Hero

RESOURCES
[1 📚 ] A logical calculus of the ideas immanent in neuron activity (McCulloch-Pitts artificial neuron): https://www.cs.cmu.edu/~./epxing/Clas...
[2 📚] The perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/co...
[3 📚] An adaptive “Adaline” neuron using chemical “memistors”: https://isl.stanford.edu/~widrow/pape...
[4 📚] Neural networks and physical systems with emergent collective computational abilities (Hopfield nets): https://pmc.ncbi.nlm.nih.gov/articles...
[5 📚] A learning algorithm for Boltzmann Machines: https://web.archive.org/web/201107180...
[6 📚] Learning representations by back propagating errors: http://www.cs.utoronto.ca/~hinton/abs...

SUPPLEMENTAL RESOURCES:
[1 📚] Action potential - a nice video explanation:    • Neuron action potential - physiology  
[2 📚 ] Nice notes with MP neurons: https://marlin.life.utsa.edu/mcculloc...
[3 📚] Synaptic Plasticity:    • PSY210 CH13PT5: Hebbian Plasticity  
[4 📚 ] Cell assembly in hebbian learning: http://www.scholarpedia.org/article/C...
[5 📚] Perceptron demo: https://sergedesmedt.github.io/MathOf...
[6 📚] Perceptron convergence theorem explained:    • Lecture 6 "Perceptron Convergence Proof" -...  
[7 📚] A symbolic analysis of relay and switching circuits: https://www.cs.virginia.edu/~evans/gr...
[8 📚 ] Geoffrey Hinton’s lecture on Hopfield Nets:    • Lecture 11/16 : Hopfield nets and Boltzman...  
[9 📚] Gibbs sampling used in learning in Boltzmann machines (video by Rithvikmath):    • Code With Me : Gibbs Sampling  
[10 📚] Lecture notes on the mathematics behind Boltzmann machines: https://www.cs.toronto.edu/~rgrosse/c...
[11 📚] Cauchy’s paper on gradient descent: http://www.kurims.kyoto-u.ac.jp/EMIS/...
[12 📚] Hinton explaining the mechanics of back propagation introduced by Rumelhart, Williams and himself:    • Lecture 3.4 — The backpropagation algorith...  
[13 📚 ] Reverse mode automatic differentiation (like back propagation): https://rufflewind.com/2016-12-30/rev...

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CHAPTERS
0:00 Introduction
00:25 1. The artificial neuron
19:46 2. Perceptron
42:12 3. ADALINE
59:01 4. Hopfield Networks
1:18:57 5. Boltzmann Machines
1:42:20 6. Multilayer Perceptron
1:55:25 7. Evolution of Back propagation
2:22:27 Conclusion