Ansible | 2020 | Video | Geerling, Jeff, , Youtube Ansible 101, Jeff Geerling/Youtube, 2020, 2020, www.youtube.com/watch?v=goclfp6a2IQ, 20240520 | In progress | A good intro to Ansible |
Complexity Theory | 2020 | Video | Aaronson, Scott, Fridman, Lex, Computational Complexity and Consciousness, Lex Fridman Podcast, Lex Fridman, 20201011, 2020, www.youtube.com/watch?v=nAMjv0NAESM, 20231201 | Reviewed | Scott Aaronson's review of the complexity zoo |
Computing | 1931 | Paper | Hirzel, Martin, Kurt Godel (Gödel), Hirzel's translation of On formally undecidable propositions of Principia Mathematica and related systems I, http://hirzels.com/martin/, Translated, 1931, 1931, hirzels.com/martin/papers/canon00-goedel.pdf, 20240103 | | Edited and translated version of Godel's paper on the incompleteness theorem. The Incompleteness Theorem paper. |
Linear Algebra | 2023 | Website | Strang, Gilbert, Introduction to Linear Algebra Sixth Edition, MIT MATH, 6, WCP, 2023, 2023, math.mit.edu/~gs/linearalgebra/ila6/indexila6.html, 20231218 | Reviewed | Website accompanying the book Introduction to Linear Algebra, 6th edition. |
Linear Algebra | 2020 | Video | Strang, Gilbert, Intro: A New Way to Start Linear Algebra, MIT OCW, MIT OpenCourseWare, 20200505, 2020, www.youtube.com/watch?v=YrHlHbtiSM0, 20231217 | Reviewed | A new way to get started with Linear Algebra A=CR, Gil Strang 2020 lectures. |
Linear Algebra | 2018 | Video | Strang, Gilbert, MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018, MIT OCW, 18.065, MIT OpenCourseWare, 20190516, 2018, www.youtube.com/watch?v=Cx5Z-OslNWE&list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k, 20231218 | In progress | Linear Algebra basics followed by concepts of stats and optimization leading into the basics of Machine Learning |
Linear Algebra | 2016 | Video | Sanderson, Grant, Essence of linear algebra, 3Blue1Brown.com, 3Blue1Brown, 20160805, 2016, www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab, 20231222 | Reviewed | Visualization of linear transformations of space, helpful aid in understanding Linear Algebra |
Linear Algebra | 2005 | Video | Strang, Gilbert, MIT OCW, MIT 18.06 Linear Algebra, Spring 2005, MIT OCW, 18.06, MIT OpenCourseWare, 20190819, 2005, www.youtube.com/playlist?list=PLE7DDD91010BC51F8, 20231218 | | Classic Linear Algebra Lectures of Gilbert Strang. |
Machine Learning | 2023 | Video | Cruise, Brit, Murray, Cameron, How Neural Networks Learned to Talk : ChatGPT: A 30 year history, Art Of the problem (YouTube), Art of the Problem, 20231127, 2023, youtu.be/OFS90-FX6pg?si=VObNh1YmdgwTn0vP, 20231224 | Reviewed | Key moments in Neural Network Research |
Machine Learning | 2023 | Video | Karpathy, Andrej, Intro to LLM, Andrej Karpathy Youtube Channel, Andrej Karpathy, 20231122, 2023, www.youtube.com/watch?v=zjkBMFhNj_g, 20231227 | Reviewed | Good intro to current state of LLM |
Machine Learning | 2023 | Website | ChatbotArena, MT-Brench, MMLU, LLM Leaderboard, huggingface.co, Huggingface.co, 2023, huggingface.co/spaces/lmsys/chatbot-arena-leaderboard, 20231227 | Reviewed | Curent state of LLM models, sorted by various scores. |
Machine Learning | 2023 | Paper | Achiam, Josh, Adler, Steven, Sandhini Agrawal, GPT-4 Techical Report, Open AI GPT, 4, 2023, 2023, arxiv.org/abs/2303.08774, 20231225 | | GPT 4 paper |
Machine Learning | 2022 | Video | Fridman, Lex, Misra, Ishan, Ishan Misra: Self-Supervised Deep Learning in Computer Vision, Lex Fridman Podcast, YouTube, Lex Fridman, 20210731, 2022, www.youtube.com/watch?v=FUS6ceIvUnI, 20240108 | | |
Machine Learning | 2022 | Paper | Kojima, Takashi, Gu, S. S., Reid et.al, Large Language Models are Zero-Shot Reasoners, NeurIPS2022, 4, 2022, 2022, arxiv.org/pdf/2205.11916.pdf, 20231225 | | |
Machine Learning | 2021 | Blog | O'Connor, Ryan, PyTorch vs TensorFlow in 2023, AssemblyAI.com, AssemblyAI.com, 2021, 2021, www.assemblyai.com/blog/pytorch-vs-tensorflow-in-2023/, 20240125 | | Library Comparison |
Machine Learning | 2021 | Blog | Misra, Ishan, LeCunn, Yann, Self Supervised Learning, The Dark Matter of Intelligence, Meta AI, Meta AI, 2021, 2021, ai.meta.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/, 20240108 | | Self Supervised Learning |
Machine Learning | 2020 | Video | Brunton, Steve, , Singular Value Decomposition, 1, Steve Brunton, 2020, 2020, www.youtube.com/watch?v=gXbThCXjZFM&list=PLMrJAkhIeNNSVjnsviglFoY2nXildDCcv, 20240317 | In Progress | |
Machine Learning | 2020 | Book | Fast.AI, Deep Learning for Coders with fastai and pytorch, fast.ai, O'Reilly Media, 2020, 2020, course.fast.ai/Resources/book.html, 20240122 | In progress | Good book for studying Deep Learning top down, a fast.ai concept. |
Machine Learning | 2020 | paper | Brown, Tom, Mann, Benjamin, Nick Ryder et.al, Language Models are Few-Shot Learners, Open AI GPT-3, 2020, 2020, arxiv.org/pdf/2005.14165.pdf, 20231225 | | GPT-3 paper |
Machine Learning | 2019 | Paper | Radford, Alec, Wu, Jeffrey, Rewon Child et. al, Language Models are Unsupervised Multitask Learners, Open AI GPT, 2019, 2019, insightcivic.s3.us-east-1.amazonaws.com/language-models.pdf, 20231225 | | The GPT and GPT-2 Paper |
Machine Learning | 2018 | Video | Ng, Andrew, Stanford CS229: Machine Learning Full Course taught by Andrew Ng Autumn 2018, Standford Online, Stanford Online, 20200417, 2018, www.youtube.com/watch?v=jGwO_UgTS7I&list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU&index=1, 20231115 | | Great primer on basics of the field of AI/ML |
Machine Learning | 2017 | Website | Smilkov, Daniel, Carter, Shan, Karpathy Andrej, A Neural Network Playground, TensorFlow.org, TensorFlow.org, 2017, playground.tensorflow.org/, 20231223 | In progress | Opensource interactive playground to try out training a neural net. |
Machine Learning | 2017 | Paper | Vaswani, Ashish, Shazeer, Noam, Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin, Attention Is All You Need, Google, arXiv, 2017, 2017, arxiv.org/pdf/1706.03762.pdf, 20231225 | | The Attention Paper |
Machine Learning | 2017 | Paper | Radford, Alec, Jozefowicz, Rafal, Sutskever Ilya, Learning to Generate Reviews and Discovering Sentiment, arXiv, 2, 2017, 2017, arxiv.org/pdf/1704.01444.pdf?, 20231225 | | The Sentiment Paper |
Machine Learning | 2016 | Book | Goodfellow, Ian, Bengio, Yoshua, Courville, Deep Learning, MIT Press, MIT Press, 2016, 2016, www.deeplearningbook.org, 20240120 | In progress | Bottom up read for deep learning |
Machine Learning | 2015 | Blog | Karpathy, Andrej, The Unreasonable Effectiveness of Recurrent Neural Networks, Andrej Karpathy's blog, May 21 2015, 2015, karpathy.github.io/2015/05/21/rnn-effectiveness/, 20231225 | | |
Machine Learning | 2014 | Paper | Goodfellow, Ian, et al., Generative Adversarial Nets, Arxiv, 2014, 2014, arxiv.org/pdf/1406.2661.pdf, | | Ian Goodellow et al. introduce generative adversarial networks (GANs). |
Machine Learning | 2012 | Paper | Krizhevsky, Alex, Sutskever, Ilya, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Neural Information Processing Systems, NIPS, 2012, 2012, proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf, 20231227 | | ImageNet Paper |
Machine Learning | 2012 | Paper | Le, Quoc V., Ranzato, Marc’Aurelio, Andrew Ng Dean Jeffery et.al, Building high-level features using large scale unsupervised learning, Google, 2012, 2012, arxiv.org/abs/1112.6209, | | Ng and Dean create a network that recognizes higher-level concepts from unlabeled images. |
Machine Learning | 2011 | Paper | Sutskever, Ilya, Martens, James, Geoffrey Hinton, Generating Text with Recurrent Neural Networks, ICML.cc, 2011, icml.cc/2011/papers/524_icmlpaper.pdf, 20231225 | | |
Machine Learning | 2006 | Paper | Hinton, Geoffrey, Salakhutdinov, Ruslan, An efficient learning procedure for Deep Learning Boltzman Machines, CMU, Neural Computation MIT, 2006, 2006, www.cs.cmu.edu/~rsalakhu/papers/neco_DBM.pdf, | | Geoffrey Hinton et al. propose using a restricted Boltzmann machine for learning high-level representations. |
Machine Learning | 1997 | Paper | Hochreiter, Sepp, Schmidhuber, Jurgen, LONG SHORT-TERM MEMORY, Neural Computation, 1997, 1997, www.bioinf.jku.at/publications/older/2604.pdf, | | Hochreiter and Schmidhuber publish LSTM recurrent neural networks. |
Machine Learning | 1997 | Paper | Domingos, Pedro, A Few Useful Things to Know about Machine Learning, Washington.edu, 1997, homes.cs.washington.edu/~pedrod/papers/cacm12.pdf, 20231216 | | Folk knowledge of state of machine Machine Learning as of 1997, a landmark paper and a good starting point read. |
Machine Learning | 1995 | Paper | Cortes, Corinna, Vapnik, Vladimir, Support-Vector Networks, Springer.com, Kluwer Academic Publishers, 1995, 1995, link.springer.com/article/10.1007/BF00994018, | | Support Vector Machines |
Machine Learning | 1992 | Paper | Schmidhuber, Jurgen, Learning Factorial Codes by Predictive minimization, idsia.ch, 1992, 1992, sferics.idsia.ch/pub/juergen/factorial.ps.gz, | | Juergen Schmidhuber proposes a hierarchy of RNNs pre-trained by self-supervised learning. |
Machine Learning | 1990 | Paper | Elman, Jeffrey L., Finding Structure in Time, UCSD, Wiley, 1990, 1990, onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog1402_1, 20231225 | | |
Machine Learning | 1989 | Paper | Waibel, Alexander, et al., Phoneme Recognition Using Time Delay Neural Network, toronto.edu, IEEE, 89, 1989, www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf, | | Alex Waibel introduces the time delay neural network (TDNN). |
Machine Learning | 1989 | Paper | LeCunn, Yann, et al., Backpropagation applied to Handwritten Zip Code Recognition, lecun.com, AT&T Bell Labs, 1989, 1989, http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf, | | Backpropagation applied to Handwritten Zip Code Recognition |
Machine Learning | 1986 | Paper | Jordan, M I., Serial order: a parallel distributed processing approach. Technical report, United States: N. p, 1986, cseweb.ucsd.edu/~gary/PAPER-SUGGESTIONS/Jordan-TR-8604-OCRed.pdf, 20231225 | | |
Machine Learning | 1982 | Paper | Hopfield, J. J., Neural networks and physical systems with emergent collective computation alabilities, pnas.org, Caltech, 1982, 1982, www.pnas.org/doi/epdf/10.1073/pnas.79.8.2554, | | In 1982, interest in the field was renewed. John Hopfield of Caltech presented a paper to the National Academy of Sciences. His approach was to create more useful machines by using bidirectional lines. Previously, the connections between neurons was only one way. (cs.standford.edu) Popularized RNN(wiki) |
Machine Learning | 1980 | Paper | Fukushima, Kunihiko, Necognitron, springer, Biological Cybernetics, 0, 1980, www.rctn.org/bruno/public/papers/Fukushima1980.pdf, | | CNNs - Neocognitron paper by Fukushima |
Machine Learning | 1969 | Paper | Fukushima, Kunihiko, Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements, IEEE, IEEE, 1969, 1969, ieeexplore.ieee.org/document/4082265, | | ReLU Activation |
Machine Learning | 1957 | Paper | Rosenblatt, Frank, The Perceptron: A Perceiving and recognition automation, UMASS.edu, Cornell, 1957, 1957, blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf, | | Frank Rosenblatt invents the perceptron, the first implemented artificial neural network. |
Machine Learning | 1950 | Paper | Turing, Alan M., COMPUTING MACHINERY AND INTELLIGENCE, UMBC.edu, Mind 49: 433-460., 1950, redirect.cs.umbc.edu/courses/471/papers/turing.pdf, 20231225 | Reviewed | The seminal Turing paper that proposes to consider, "Can Machines Think?". The Turing Test/Immitation Game paper. |
Machine Learning | 1949 | Book | Hebb, D. O., The organization of behavior, Psychology press, 1949, 1949, en.wikipedia.org/wiki/Organization_of_Behavior#CITEREFWebster2005, 20230105 | | D. O. Hebb[12] created a learning hypothesis based on the mechanism of neural plasticity that became known as Hebbian learning.(wikipedia) |
Machine Learning | 1943 | Paper | McCulloch, Warren S., Pitts, Walter, A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical BioPhysics Volume 5, 1943, 1943, 1943, home.csulb.edu/~cwallis/382/readings/482/mccolloch.logical.calculus.ideas.1943.pdf, 20240104 | In progress | A model of a simple neural network |
Machine Learning | 1800 | Wiki | Wikipedia, Wikipedia, Wiki on Least Squares, WikiPedia, WikiPedia, 1800, en.wikipedia.org/wiki/Least_squares, 20240104 | | Placeholder: Gauss uses method of least squares for predicting planetary movement. Legendre uses method of least squares for linear fit to points. There was a disput on Priority |
Mathematics | 2022 | Video | Sanderson, Grant, But what is Convolution?, 3Blue1Brown YouTube Channel, 3Blue1Brown, 20221118, 2022, www.youtube.com/watch?v=KuXjwB4LzSA, 20240107 | | Basics of convolution |
Mathematics | 2008 | Video | Osgood, Brad, The Fourier Transforms and its Applications, Standford Online Youtube, EE261, 2008, www.youtube.com/playlist?list=PLB24BC7956EE040CD, 20230111 | In progress | Graduate level course on Fourier series, and analysis |
Quantum Computing | 2020 | Video | Fridman, Lex, Aaronson, Scott, Scott Aaronson: Quantum Computing, Lex Fridman Podcast, #72 Round 1, Youtube.com, Feb 17 2020, 2020, www.youtube.com/watch?v=uX5t8EivCaM, Nov 26 2020 | Reviewed | A summary description of the state of Quantum Computing as of 2020, post quantum supremacy. |
Quantum Mechanics | 2020 | Video | Greene, Brian, Your Daily Equation #21: Bell's Theorem and the Non-locality of the Universe, World Science Festival, World Science Festival, 20200504, 2020, www.youtube.com/watch?v=UZiwtfrisTQ, 20231101 | Reviewed | A good visual explanation of Bell's inequality and the story of the EPR paper, by Brian Greene. |
Quantum Mechanics | 2012 | Video | Susskind, Leonard, Lecture Collection The Theoretical Minimum: Quantum Mechanics, Stanford Online, Stanford, 20120216, 2012, www.youtube.com/watch?v=iJfw6lDlTuA&list=PL701CD168D02FF56F, 20230601 | Reviewed | Leonard Susskind's classic Theoritcal Minimum lecture series. A great way to get started on QM. |
Quantum Mechanics | 2006 | Video | Susskind, Leonard, Quantum Entanglements: Part 1 (Fall 2006), Standford Online, 2006, www.youtube.com/playlist?list=PLA27CEA1B8B27EB67, 20231115 | Reviewed | Leonard Susskind's classic lectures on Quantum Entanglement |
Quantum Physics | 2023 | Video | Riordon, James, The ghost particle: searching for the mysterious neutrino - with James Riordon, The Royal Institution, The Royal Institution, 20231129, 2023, www.youtube.com/watch?v=8cgpXThIDtc, 20231201 | Reviewed | History and the state of science of Neutrinos |
Research Tools | | Website | MLA General Format, Purdue OWL® - Purdue University, owl.purdue.edu/owl/research_and_citation/mla_style/mla_formatting_and_style_guide/mla_general_format.html, Nov 19 2023 | Reviewed | Basics of how to format a citation in the MLA format. |
Research Tools | 2018 | Video | Ng, Andrew, Stanford CS230: Deep Learning Autumn 2018 Lecture 8 - Career Advice / Reading Research Papers, Stanford Online, Stanford Online, 20190403, 2018, www.youtube.com/watch?v=733m6qBH-jI, 20231101 | Reviewed | Advice on how to approach reading Machine Learning papers. |
Technology | | Video | Cavanaugh, Sean, Ansible Basics: Automation Technical Overview, Redhat.com, 1, Redhat.com, rhtapps.redhat.com/promo/course/do007, 20240428 | In progress | Ansible Automation Basics |