# 발표자료에 인용한 자료들 출처 "Lee Sedol (B) vs AlphaGo (W), 2016, Game 1", Wesalius. commons.wikimedia.org/wiki/File:Lee_Sedol_%28B%29_vs_AlphaGo_%28W%29_-_Game_1.svg “DQN Breakout”, DeepMind. ruclips.net/video/TmPfTpjtdgg/видео.html "Deep Blue versus Kasparov, 1996, Game 1", Morn. commons.wikimedia.org/wiki/File:Deep_Blue_versus_Kasparov,_1996,_Game_1.gif "Mastering the game of Go with deep neural networks and tree search.", Silver, D., Huang, A., Maddison, C. et al. Nature 529, 484-489 (2016). doi.org/10.1038/nature16961 “Error rate history on ImageNet (showing best result per team and up to 10 entries per year)”, Gkrusze. en.wikipedia.org/wiki/ImageNet#/media/File:ImageNet_error_rate_history_(just_systems).svg “ImageNet: A Large-Scale Hierarchical Image Database”, Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei. www.image-net.org/challenges/LSVRC/2010/index.php “ImageNet Classification with Deep Convolutional Neural Networks”, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton. papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html “NVIDIA GTX 580 Running Crysis!”, Motherboards.org. ruclips.net/video/ifF1CVq5xUY/видео.html “SMI32-stained pyramidal neurons in cerebral cortex”, UC Regents Davis campus. en.wikipedia.org/wiki/Neuron#/media/File:Smi32neuron.jpg “Structure of a typical neuron”, Mauro Lanari. en.wikipedia.org/wiki/Neuron#/media/File:Neuron_Hand-tuned2.svg “Artificial neural network with layer coloring”, Glosser.ca. en.wikipedia.org/wiki/Neural_network_(machine_learning)#/media/File:Colored_neural_network.svg “A Neural Network Playground”, Daniel Smilkov, Shan Carter. playground.tensorflow.org “Agent57: Outperforming the human Atari benchmark”, Adrià Puigdomènech, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell. deepmind.google/discover/blog/agent57-outperforming-the-human-atari-benchmark/ “Teachable Machine”, Google. teachablemachine.withgoogle.com/ “Breaking the curse of small datasets in Machine Learning: Part 1”, Jyoti Prakash Maheswari. towardsdatascience.com/breaking-the-curse-of-small-datasets-in-machine-learning-part-1-36f28b0c044d “What Is Overfitting?”, MathWorks. www.mathworks.com/discovery/overfitting.html “CIFAR-10 and CIFAR-100 datasets”, Alex Krizhevsky. www.cs.toronto.edu/~kriz/cifar.html “Unit 1. Introduction to Deep Reinforcement Learning”, Thomas Simonini. huggingface.co/learn/deep-rl-course/unit1/hands-on “Unit 2. Introduction to Q-Learning”, Thomas Simonini. huggingface.co/learn/deep-rl-course/unit2/mc-vs-td “Training AI to Play Pokemon with Reinforcement Learning”, Peter Whidden. ruclips.net/video/DcYLT37ImBY/видео.html “t-SNE Map”, Cyril Diagne, Nicolas Barradeau & Simon Doury. experiments.withgoogle.com/t-sne-map “Deep Learning in a Nutshell: Core Concepts”, Tim Dettmers. developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/ “Computer Vision - What Is it and Why Does It Matter?”, NVIDIA. www.nvidia.com/en-us/glossary/computer-vision/ "The AI feedback loop: Researchers warn of ‘model collapse’ as AI trains on AI-generated content", Carl Franzen. venturebeat.com/ai/the-ai-feedback-loop-researchers-warn-of-model-collapse-as-ai-trains-on-ai-generated-content/ “Generation loss: FLIF vs WebP vs BPG vs JPEG”, Jon Sneyers. ruclips.net/video/_h5gC3EzlJg/видео.html “THE CURSE OF RECURSION: TRAINING ON GENERATED DATA MAKES MODELS FORGET”, Ilia Shumailov. arxiv.org/pdf/2305.17493 “Illustrated Guide to Transformer”, Hong Jing. jinglescode.github.io/2020/05/27/illustrated-guide-transformer/ “Improving language understanding with unsupervised learning”, Alec Radford et al. openai.com/index/language-unsupervised/ “How GPT3 Works - Visualizations and Animations”, Jay Alammar. jalammar.github.io/how-gpt3-works-visualizations-animations/ “Improving language understanding with unsupervised learning”, Alec Radford et al. openai.com/index/language-unsupervised/ “Naver sentiment movie corpus v1.0”, Lucy Park. github.com/e9t/nsmc/ “Transformer Explainer”, Aeree Cho et al. poloclub.github.io/transformer-explainer/ “Aligning language models to follow instructions”, Ryan Lowe, Jan Leike. openai.com/index/instruction-following/ “Shoggoth with Smiley Face”, @anthrupad x.com/anthrupad/status/1622349563922362368 “Language Models are Few-Shot Learners”, Tom B. Brown et al. arxiv.org/abs/2005.14165 “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”, Jason Wei et al. arxiv.org/abs/2201.11903 “Large Language Models are Zero-Shot Reasoners”, Takeshi Kojima et al. arxiv.org/abs/2205.11916 “Deep Neural Networks for RUclips Recommendations”, Paul Covington, Jay Adams, Emre Sargin. static.googleusercontent.com/media/research.google.com/ko//pubs/archive/45530.pdf
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# 발표자료에 인용한 자료들 출처
"Lee Sedol (B) vs AlphaGo (W), 2016, Game 1", Wesalius.
commons.wikimedia.org/wiki/File:Lee_Sedol_%28B%29_vs_AlphaGo_%28W%29_-_Game_1.svg
“DQN Breakout”, DeepMind.
ruclips.net/video/TmPfTpjtdgg/видео.html
"Deep Blue versus Kasparov, 1996, Game 1", Morn.
commons.wikimedia.org/wiki/File:Deep_Blue_versus_Kasparov,_1996,_Game_1.gif
"Mastering the game of Go with deep neural networks and tree search.", Silver, D., Huang, A., Maddison, C. et al. Nature 529, 484-489 (2016).
doi.org/10.1038/nature16961
“Error rate history on ImageNet (showing best result per team and up to 10 entries per year)”, Gkrusze.
en.wikipedia.org/wiki/ImageNet#/media/File:ImageNet_error_rate_history_(just_systems).svg
“ImageNet: A Large-Scale Hierarchical Image Database”, Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei.
www.image-net.org/challenges/LSVRC/2010/index.php
“ImageNet Classification with Deep Convolutional Neural Networks”, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton.
papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
“NVIDIA GTX 580 Running Crysis!”, Motherboards.org.
ruclips.net/video/ifF1CVq5xUY/видео.html
“SMI32-stained pyramidal neurons in cerebral cortex”, UC Regents Davis campus.
en.wikipedia.org/wiki/Neuron#/media/File:Smi32neuron.jpg
“Structure of a typical neuron”, Mauro Lanari.
en.wikipedia.org/wiki/Neuron#/media/File:Neuron_Hand-tuned2.svg
“Artificial neural network with layer coloring”, Glosser.ca.
en.wikipedia.org/wiki/Neural_network_(machine_learning)#/media/File:Colored_neural_network.svg
“A Neural Network Playground”, Daniel Smilkov, Shan Carter.
playground.tensorflow.org
“Agent57: Outperforming the human Atari benchmark”,
Adrià Puigdomènech, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell.
deepmind.google/discover/blog/agent57-outperforming-the-human-atari-benchmark/
“Teachable Machine”, Google.
teachablemachine.withgoogle.com/
“Breaking the curse of small datasets in Machine Learning: Part 1”, Jyoti Prakash Maheswari.
towardsdatascience.com/breaking-the-curse-of-small-datasets-in-machine-learning-part-1-36f28b0c044d
“What Is Overfitting?”, MathWorks.
www.mathworks.com/discovery/overfitting.html
“CIFAR-10 and CIFAR-100 datasets”, Alex Krizhevsky.
www.cs.toronto.edu/~kriz/cifar.html
“Unit 1. Introduction to Deep Reinforcement Learning”, Thomas Simonini.
huggingface.co/learn/deep-rl-course/unit1/hands-on
“Unit 2. Introduction to Q-Learning”, Thomas Simonini.
huggingface.co/learn/deep-rl-course/unit2/mc-vs-td
“Training AI to Play Pokemon with Reinforcement Learning”, Peter Whidden.
ruclips.net/video/DcYLT37ImBY/видео.html
“t-SNE Map”, Cyril Diagne, Nicolas Barradeau & Simon Doury.
experiments.withgoogle.com/t-sne-map
“Deep Learning in a Nutshell: Core Concepts”, Tim Dettmers.
developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/
“Computer Vision - What Is it and Why Does It Matter?”, NVIDIA.
www.nvidia.com/en-us/glossary/computer-vision/
"The AI feedback loop: Researchers warn of ‘model collapse’ as AI trains on AI-generated content", Carl Franzen.
venturebeat.com/ai/the-ai-feedback-loop-researchers-warn-of-model-collapse-as-ai-trains-on-ai-generated-content/
“Generation loss: FLIF vs WebP vs BPG vs JPEG”, Jon Sneyers.
ruclips.net/video/_h5gC3EzlJg/видео.html
“THE CURSE OF RECURSION: TRAINING ON GENERATED DATA MAKES MODELS FORGET”, Ilia Shumailov.
arxiv.org/pdf/2305.17493
“Illustrated Guide to Transformer”, Hong Jing.
jinglescode.github.io/2020/05/27/illustrated-guide-transformer/
“Improving language understanding with unsupervised learning”, Alec Radford et al.
openai.com/index/language-unsupervised/
“How GPT3 Works - Visualizations and Animations”, Jay Alammar.
jalammar.github.io/how-gpt3-works-visualizations-animations/
“Improving language understanding with unsupervised learning”, Alec Radford et al.
openai.com/index/language-unsupervised/
“Naver sentiment movie corpus v1.0”, Lucy Park.
github.com/e9t/nsmc/
“Transformer Explainer”, Aeree Cho et al.
poloclub.github.io/transformer-explainer/
“Aligning language models to follow instructions”, Ryan Lowe, Jan Leike.
openai.com/index/instruction-following/
“Shoggoth with Smiley Face”, @anthrupad
x.com/anthrupad/status/1622349563922362368
“Language Models are Few-Shot Learners”, Tom B. Brown et al.
arxiv.org/abs/2005.14165
“Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”, Jason Wei et al.
arxiv.org/abs/2201.11903
“Large Language Models are Zero-Shot Reasoners”, Takeshi Kojima et al.
arxiv.org/abs/2205.11916
“Deep Neural Networks for RUclips Recommendations”, Paul Covington, Jay Adams, Emre Sargin.
static.googleusercontent.com/media/research.google.com/ko//pubs/archive/45530.pdf
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