TensorFlow Projects

This github repository holds references for, and scripts resulting from, various TensorFlow projects

• The TensorFlow path to enlightenment

• Google - Do It Yourself AI - Vision, Voice, Maker Kits

• Google - Colab Tutorials for Coral

General Observations

• 'machine learning provides pattern recognition'

• 'convert an image/sound/phrase/spectra (Godel number?) into a numerical representation'

• 'determine the activities and write the code that matches the data to the labels'

• 'Competence without comprehension is a strategy in nature and perhaps in machine learning as well.'

Videos

• The TensorFlow Youtube channel

• Tensorflow in 100 seconds - Fireship

• Machine Learning for Raspberry Pi - Kang

• Machine Learning for Everybody-Kylie Ying

• Intro to Large Language Models - Andrej Karpathy

Articles

• Open-source AI chatbots are booming — what does this mean for researchers? - Davide Castelvecchi - 2023 - Nature

Papers

• Perceptron: A Perceiving and Recognizing Automaton - Frank Rosenblatt - 1957 - Umass edu

• Learning representations by back-propagating errors -David E. Rumelhart, Geoffrey E. Hinton & Ronald J. Williams - 1986 - U Montreal

• 🐺 Alopex: A Correlation-Based Learning Algo for Feedforward and Recurrent Neural Networks - K.P.Unnikrishnan, K.P. Venugopal - 1994 - CSU edu

• Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - 2015 - arXiv:1512.03385 (cs)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications - Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam - April, 2017 - arXiv:1704.04861 [cs.CV]

• Attention Is All You Need -Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin - June 2017 - arXiv:1706.03762 [cs.CL]

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms - Han Xiao, Kashif Rasul, Roland Vollgraf - August, 2017 - arXiv:1708.07747 [cs.LG]

  • Dataset used in the Xiao et al Paper - Github

• On the Measure of Intelligence François Chollet - 2019 - arXiv:1911.01547 (cs)

• High-Resolution Image Synthesis with Latent Diffusion Models - Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer - 2021 - arXiv:2112.10752 [cs.CV]

  • Code from the Rombach et al Paper - Github

• Llama 2: Open Foundation and Fine-Tuned Chat Models - Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom - 2023 - arXiv:2307.09288 [cs.CL]

• Emergent Analogical Reasoning in Large Language Models - Taylor Webb, Keith J. Holyoak, Hongjing Lu - arXiv:2212.09196 [cs.AI]

Books

• Make Your Own Neural Network - Tariq Rashid
• AI and Machine Learning for Coders - Laurence Moroney
• AI and Machine Learning for On-Device Development - Laurence Moroney
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition - Aurelien Geron
• Practical Deep Learning for Cloud, Mobile, and Edge - Anirudh Koul, Siddha Ganju, Meher Kasam
• Building Machine Learning Powered Applications - Emmanuel Ameisen
• Machine Learning with Python Cookbook - Chris Albon
• No BS Guides to Linear Algebra, Math, and Physics - Ivan Savov

MOOCs

• Coursera

• Dr. Andrew Ng's courses

• MOOC dot org

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IDE

Google Colab notebooks are Jupyter notebooks

Colab, or "Colaboratory", allows you to write and execute Python in your browser, with

• Zero configuration required
• Access to GPUs free of charge
• Easy sharing

JupyterLab is a web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning.