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Title: GitHub - CalculatedContent/WeightWatcher: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

Open Graph Title: GitHub - CalculatedContent/WeightWatcher: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

X Title: GitHub - CalculatedContent/WeightWatcher: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

Description: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks - CalculatedContent/WeightWatcher

Open Graph Description: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks - CalculatedContent/WeightWatcher

X Description: The WeightWatcher tool for predicting the accuracy of Deep Neural Networks - CalculatedContent/WeightWatcher

Opengraph URL: https://github.com/CalculatedContent/WeightWatcher

X: @github

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http://pepy.tech/project/weightwatcher
https://pypi.org/project/weightwatcher/
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/LICENSE.txt
https://nature.com/articles/s41467-021-24025-8
https://www.youtube.com/watch?v=Tnafo6JVoJs
https://discord.gg/uVVsEAcfyF
https://www.linkedin.com/in/charlesmartin14/
https://www.calculatedcontent.com
https://weightwatcher.ai
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#quick-links
our latest talk from the Sillicon Valley ACM meetuphttps://www.youtube.com/watch?v=Tnafo6JVoJs
Discord Serverhttps://discord.gg/uVVsEAcfyF
invited talk at NeurIPS 2023https://youtu.be/xEuBwBj_Ov4
https://arxiv.org/abs/2507.17912https://arxiv.org/abs/2507.17912
https://arxiv.org/abs/2506.04434https://arxiv.org/abs/2506.04434
Practical AIhttps://changelog.com/practicalai/194
The Prompt Deskhttps://smartlink.ausha.co/the-prompt-desk/data-free-quality-analysis-of-deep-neural-nets-with-charles-h-martin
The TWIML AI Podcast with Sam Charringtonhttps://www.youtube.com/watch?v=pxPCKR6ED4s
Calculated Content Bloghttps://calculatedcontent.com/
https://weightwatcher.aihttps://weightwatcher.ai
WeightWatcher-examples github repohttps://github.com/CalculatedContent/WeightWatcher-examples
WeightWatcher-Prohttps://weightwatcher-ai.com
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#installation--version-076
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#current-testpypi--version-0755
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#usage
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/img/sample-ww-details.png
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#advanced-usage
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#peft--lora-models--experimental
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#usage-base-model
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/img/ww0.7.4.jpeg
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#ploting-and-fitting-the-empirical-spectral-density-esd
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/img/ESD-plots.png
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#generalization-metrics
Our HTSR theoryhttps://jmlr.org/papers/volume22/20-410/20-410.pdf
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#scale-metrics
https://render.githubusercontent.com/render/math?math=\log_{10}\Vert\mathbf{W}\Vert^{2}_{F}
https://render.githubusercontent.com/render/math?math=\log_{10}\lambda_{max}=\log_{10}\Vert\mathbf{W}\Vert^{2}_{\infty}
https://render.githubusercontent.com/render/math?math=R_{stable}=\Vert\mathbf{W}\Vert^{2}_{F}/\Vert\mathbf{W}\Vert^{2}_{\infty}
https://render.githubusercontent.com/render/math?math=R_{MP}=\lambda_{MP}/\lambda_{max}
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#shape-metrics
https://render.githubusercontent.com/render/math?math=\alpha
https://render.githubusercontent.com/render/math?math=\D
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#scale-adjusted-shape-metrics
https://render.githubusercontent.com/render/math?math=\hat{\alpha}=\alpha\log_{10}\lambda_{max}
https://render.githubusercontent.com/render/math?math=\log_{10}\Vert\mathbf{X}\Vert^{\alpha}_{\alpha}
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#direct-correlation-metrics
See this recent blog posthttps://calculatedcontent.com/2021/10/17/fantastic-measures-of-generalization-that-actually-work-part-1/
https://render.githubusercontent.com/render/math?math=div(\mathbf{W},rand(\mathbf{W}))
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#misc-details
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#summary-statistics
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#predicting-the-generalization-error
https://github.com/CalculatedContent/PredictingTestAccuracies/blob/master/img/vgg-w_alphas.png
VGGhttps://github.com/CalculatedContent/WeightWatcher/blob/master/examples/WW-VGG.ipynb
ResNethttps://github.com/CalculatedContent/WeightWatcher/blob/master/examples/WW-ResNet.ipynb
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#detecting-signs-of-over-fitting-and-under-fitting
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#correlation-traps
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/img/correlation_trap.jpeg
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#early-stopping
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/img/early_stopping.png
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#additional-features
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#filtering
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#filter-by-layer-types
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#filter-by-layer-id-or-name
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#calculations
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#minimum-maximum-number-of-eigenvalues-of-the-layer-weight-matrix
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#specify-the-power-law-fitting-proceedure
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#visualization
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#save-all-model-figures
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#fit-esds-to-a-marchenko-pastur-mp-distrbution
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#fetch-the-esd-for-a-specific-layer-for-visualization-or-additional-analysis
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#model-analysis
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https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#compatability-with-version-02x
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#requirements
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#frameworks-supported
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#layers-supported
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#tips-for-first-time-users
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#how-to-release
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#license
Apache License 2.0https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/LICENSE.txt
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#academic-presentations-and-media-appearances
Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metricshttps://arxiv.org/abs/2106.00734
Evaluating natural language processing models with robust generalization metrics that do not need access to any training or testing datahttps://arxiv.org/abs/2202.02842
(Nature paper) Predicting trends in the quality of state-of-the-art neural networks without access to training or testing datahttps://www.nature.com/articles/s41467-021-24025-8
Repo for Nature paperhttps://github.com/CalculatedContent/ww-trends-2020
(JMLR in press) Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learninghttps://arxiv.org/abs/1810.01075
Traditional and Heavy Tailed Self Regularization in Neural Network Modelshttps://arxiv.org/abs/1901.08276
https://github.com/CalculatedContent/ImplicitSelfRegularizationhttps://github.com/CalculatedContent/ImplicitSelfRegularization
ICML 2019 Theoretical Physics Workshop Paperhttps://github.com/CalculatedContent/PredictingTestAccuracies/blob/master/ICMLPhysicsWorkshop/icml_prl_TPDLW2019_fin.pdf
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networkshttps://arxiv.org/abs/1901.08278
https://github.com/CalculatedContent/PredictingTestAccuracieshttps://github.com/CalculatedContent/PredictingTestAccuracies
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behaviorhttps://arxiv.org/abs/1710.09553
NERSC Summer 2018https://www.youtube.com/watch?v=_Ni5UDrVwYU
UC Berkeley/ICSI 12/13/2018https://www.youtube.com/watch?v=6Zgul4oygMc
Institute for Pure & Applied Mathematics (IPAM)https://www.youtube.com/watch?v=fmVuNRKsQa8
Physics Informed Machine Learninghttps://www.youtube.com/watch?v=eXhwLtjtUsI
Talk at Stanford ICME 2020https://www.youtube.com/watch?v=PQUItQi-B-I
Talk at UCL (UK) 2022https://www.youtube.com/watch?v=sOXROWJ70Pg
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#kdd2019-workshop
KDD 2019 Workshop: Statistical Mechanics Methods for Discovering Knowledge from Production-Scale Neural Networkshttps://dl.acm.org/doi/abs/10.1145/3292500.3332294
KDD 2019 Workshop: Slideshttps://www.stat.berkeley.edu/~mmahoney/talks/dnn_kdd19_fin.pdf
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#popular-popdcasts-and-blogs
This Week in MLhttps://twimlai.com/meetups/implicit-self-regularization-in-deep-neural-networks/
Data Science at Home Podcasthttps://podcast.datascienceathome.com/e/episode-70-validate-neural-networks-without-data-with-dr-charles-martin/
Aggregate Intellect VLoghttps://aisc.ai.science/events/2019-11-06
Rebellion Research VLoghttps://blog.rebellionresearch.com/blog/theoretical-physicist-dr-charles-martin-on-deep-learning
Rebellion Research BLoghttps://www.rebellionresearch.com/why-does-deep-learning-work
LightOn AI Meetuphttps://www.youtube.com/watch?v=tciq7t3rj98
The Sillicon Valley ACM meetuphttps://www.youtube.com/watch?v=Tnafo6JVoJs
Applied AI Communityhttps://www.youtube.com/watch?v=xLZOf2IDLkc&feature=youtu.be
Practical AIhttps://changelog.com/practicalai/194
Latest Resultshttps://www.youtube.com/watch?v=rojbXvK9mJg
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#2021-short-presentations
MLC Research Jam March 2021https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/presentations/ww_5min_talk.pdf
PyTorch2021 Poster April 2021https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher/blob/master/presentations/pytorch2021_poster.pdf
https://patch-diff.githubusercontent.com/CalculatedContent/WeightWatcher#recent-talks-by-mike-mahoney-uc-berekely
IARAI, the Institute for Advanced Research in Artificial Intelligencehttps://www.youtube.com/watch?v=Pirni67ZmRQ
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Charles H Martin, PhDhttps://www.linkedin.com/in/charlesmartin14
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