Hopfield Networks - Explained!

Let's understand the architecture that led to John Hopfield's noble prize. RESOURCES [1 📚] Main paper: https://pmc.ncbi.nlm.nih.gov/articles/instance/346238/pdf/pnas00447-0135.pdf [2 📚] Geoffrey Hinton’s lecture on Hopfiled Nets: https://www.youtube.com/watch?v=IP3W7cI01VY&list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0&index=11 [3 📚] Why physical systems minimize total energy: https://physics.stackexchange.com/questions/255353/why-does-a-system-try-to-minimize-its-total-energy [4 📚] Entropy: https://www.youtube.com/watch?v=DxL2HoqLbyA [5 📚] Lecture notes on applications of Hopfield networks: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e97b368e1d9e68a930bd6efb822ab0a8980e95a4 [6 📚] Paper that proved the memory capacity of hopfield nets is 0.14N: https://www.physics.rutgers.edu/~morozov/568_s2020/Physics_568_2020_files/Amit_Sompolinsky_PhysRevLett1985.pdf [7 📚] Historical notes in the end seemed useful: https://axon.cs.byu.edu/~martinez/classes/678/Papers/Hopfield_Chapter.pdf [8 📚] Hopfield and Lex talking about associative memory: https://www.youtube.com/watch?v=aDHWbgEufYU ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ PLAYLISTS FROM MY CHANNEL ⭕ Deep Learning 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_NwyY_PeSYrYfsvHZnHGPU ⭕ Natural Language Processing 101: https://www.youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: https://imp.i384100.net/Tensorflow CHAPTERS 0:00 Why Hopfield Networks? 2:18 How humans store and recall memories 4:05 How Hopfield network is organized 5:34 How Hopfield network stores memory? 7:37 How Hopfield network recalls memory? 16:00 Energy landscape 16:31 Pros and Cons of Hopfield networks 18:42 Quiz Time 19:43 Summary