Resources

The following is a collection of the resources that I came across and have found useful. It contains pointers to articles, tutorials, data, etc. on such topics as open data, Python, Jupyter Notebook, machine learning, open source code/software, inversion, etc.  This webpage is constantly under construction, as I will keep updating it when I see something relevant and useful.  Stay tuned for the latest updates! 

Note that some of the links might be outdated or become invalid. If you notice any of this, please let me know. Or, you know some resources that I should post here, please also let me know.

Open data

1. Research data from SEG: http://seg.org/News-Resources/Research-Data

2. Open data from NASA: https://open.nasa.gov/open-data/

3. US government's open data: https://catalog.data.gov/dataset?tags=earth+science

4. Oil & Gas Well Records – GIS Well Logs: http://www.rrc.state.tx.us/oil-gas/research-and-statistics/obtaining-commission-records/oil-gas-well-records-gis-well-logs/

5. Awesome-Open-Geoscience: https://github.com/softwareunderground/awesome-open-geoscience#data-repositories

6. Marine geoscience data system: http://www.marine-geo.org/index.php

7. USGS Science data catalog: https://data.usgs.gov/datacatalog/ 

8. List of public data sources fit for machine learning: https://blog.bigml.com/list-of-public-data-sources-fit-for-machine-learning/

9. 33 Brilliant and free data sources: https://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#195a416b54db

10. 20 Open geosciene database: https://mp.weixin.qq.com/s/sXd_BSShi0CZQYUBt8wWPg

11. Mendeley datasets for PEPI: https://www.journals.elsevier.com/physics-of-the-earth-and-planetary-interiors/mendeley

12. Linear regression datasets: http://people.sc.fsu.edu/~jburkardt/datasets/regression/regression.html

13. USGS Publications Warehouse http://pubs.er.usgs.gov/

14. NOAA Trackline Geophysical Data http://maps.ngdc.noaa.gov/viewers/geophysics/

15. Magnetic anomaly map for North America: http://mrdata.usgs.gov/magnetic/; http://mrdata.usgs.gov/geophysics/map.html; http://pubs.usgs.gov/of/2002/ofr-02-414/; ftp://ftpext.usgs.gov/pub/cr/co/denver/musette/pub/open-file-reports/ofr-02-0414/

16. NOAA Airborne Magnetic DATA http://www.ngdc.noaa.gov/geomag/aromag.shtml

17. USGS Data Release Products: https://www.sciencebase.gov/catalog/item/5474ec49e4b04d7459a7eab2; https://www.sciencebase.gov/catalog/

18. Public geophysical data set: https://gis.geosurv.gov.nl.ca/; http://qdexdata.dnrm.qld.gov.au/flamingo/

19. Free online interactive data visualizations with data from the Ocean Observatories Initiative (OOI): https://datalab.marine.rutgers.edu/data-labs/data-explorations/  

20. Gravity and magnetic data used in Astic et al. (2020): https://zenodo.org/record/3571472#.X0SV1MhKjD4 The joint inversion codes are available from https://github.com/simpeg-research/Astic-2020-JointInversion 

21. Mineral deposits of MRS: https://wim.usgs.gov/geonarrative/MRS_mineral_deposits/ 

22. Paleomag https://dinosaurpictures.org/ancient-earth#150; http://www.scotese.com/

23. Nebraska Geocloud: https://snr.unl.edu/csd/geology/nebraskageocloud.aspx

24. Experimental gravity field model XGM2016: http://doi.org/10.5880/icgem.2017.003

25. Gravity field models of other celestial bodies: http://icgem.gfz-potsdam.de/tom_celestial

26. Open ML data sets: https://paperswithcode.com/datasets

27. USGS Mineral Resources Online Spatial Data: https://mrdata.usgs.gov/

28. USGS Mineral Resource Online Geophysical Data: https://mrdata.usgs.gov/#geophysics

29. Mineral deposits of the Mid-continental Rift System: https://geonarrative.usgs.gov/mrs_mineral_deposits/

Critical Minerals

1. Canada’s critical minerals strategy: Discussion paper: https://www.canada.ca/en/campaign/critical-minerals-in-canada/canada-critical-minerals-strategy-discussion-paper.html

2. Policy paper - Resilience for the Future: The UK’s critical minerals strategy: https://www.gov.uk/government/publications/uk-critical-mineral-strategy/resilience-for-the-future-the-uks-critical-minerals-strategy#foreword-from-the-secretary-of-state-for-business-energy-and-industrial-strategy

Geophysics 

1. The inverse modeling and geostatistics project: http://imgp.nbi.dk/papers.php

2. OCCAM1DCSEM-An inversion program for generating smooth 1D models from CSEM and MT http://marineemlab.ucsd.edu/projects/occam/1DCSeM/

3. OCCAM2DMT: http://marineemlab.ucsd.edu/Projects/Occam/2DMT/index.html

4. 1D MT forward modeling tutorial http://www.digitalearthlab.com/tutorial/tutorial-1d-mt-forward/

5. Center for Computational Geostatistics (CCG) http://www.ccgalberta.com/

6. CREWES Joint PP and PS inversion http://www.crewes.org/ResearchLinks/JointInversion/

7. mGstat: A Geostatistical Matlab toolbox http://mgstat.sourceforge.net/

8. SGeMS: Stanford Geostatistical Modeling Software http://sgems.sourceforge.net/?q=node/82

9. Gianluca Fiandaca publications http://pure.au.dk/portal/en/persons/gianluca-fiandaca%28186f527c-7d47-4c9e-8b9d-aa628a435c13%29/publications.html

10. Inverse and ill-posed problems series http://www.degruyter.com/view/serial/22588

11. Jeannot Tranpert’s homepage http://www.geo.uu.nl/~jeannot/My_web_pages/Research_Interests/Research_Interests.html

12. Geoscience BC Quest project: http://www.geosciencebc.com/s/2009-15.asp

13. Princeton Theoretical & Computational Seismology software http://www.princeton.edu/geosciences/tromp/software/

14. Fredrik Simons software http://geoweb.princeton.edu/people/simons/software.html

15. Computational Infrastructure for Geodynamics software http://geodynamics.org/cig/software/specfem3d/; http://wiki.geodynamics.org/software:specfem3d:start

16. FAST http://terra.rice.edu/department/faculty/zelt/fast.html

17. CREWES Matlab Toolbox http://www.crewes.org/ResearchLinks/FreeSoftware/

18. SeisLab 3.01 http://www.mathworks.com/matlabcentral/fileexchange/15674-seislab-3-01

19. SeismicLab http://seismic-lab.physics.ualberta.ca/

20. SEG Software http://software.seg.org/

21. Spice http://www.spice-rtn.org/library/software.1.html

22. ERL Lab Reports http://erl.mit.edu/lab-reports.php

23. Jon Claerbout’s Classroom http://sepwww.stanford.edu/sep/prof/index.html

24. CREWES Graduate Theses http://www.crewes.org/ResearchLinks/GraduateTheses/

25. CWP Research Reports http://cwp.mines.edu/researchpublications/CWPresearchreports.html

26. Samizdat Press http://samizdat.mines.edu/

27. SEP Reports http://sepwww.stanford.edu/doku.php?id=sep:research:research

28. Madagascar reproducible documents http://www.ahay.org/wiki/Reproducible_Documents

29. WaveLab 850 http://statweb.stanford.edu/~wavelab/Wavelab_850/index_wavelab850.html

30. MTNet http://mtnet.dias.ie/main/source.html

31. MT at Oregon State University http://hendrix2.uoregon.edu/~dlivelyb/lpe_talk/mt_intro.html

32. Most Downloaded Journal of Applied Geophysics Articles http://www.journals.elsevier.com/journal-of-applied-geophysics/most-downloaded-articles/

33. Nick Rawlinson http://www.abdn.ac.uk/geosciences/people/profiles/nrawlinson

34. SPECFEM3D http://specfem3d.readthedocs.org/en/latest/

35. GRACE Tellus: http://grace.jpl.nasa.gov/data/get-data/; http://grace.jpl.nasa.gov/publications/

36. Gravity Observations Combination (GOCO) http://www.goco.eu/

37. TOAST (toolbox for applied seismic tomography) http://www.opentoast.de

38. SimPEG: Simulation and Parameter estimation in Geophysics: http://simpeg-dc.readthedocs.io/en/latest/index.html; http://www.sciencedirect.com/science/article/pii/S009830041530056X

39. PRONTO seismic traveltime inversion: http://cgiss.boisestate.edu/~billc/TomoDocs/pronto.html

40. MGinv3D http://www.scicomap.com/MGinv3D.html

41. 2D MT inversion code: MT2DInvMatlab http://www.sciencedirect.com/science/article/pii/S009830040900106X

42. Open source 1D CSEM inversion code: http://www-old.dpr.csiro.au/StochasticSeismicInversion/

43. Matlab code for 3D MT forward modeling and inversion: http://www.sciencedirect.com/science/article/pii/S0098300417303539

44. A unified 2D/3d large scale software environment for nonlinear inverse problems: https://arxiv.org/pdf/1703.09268.pdf

45. WHAM: Web Hosted Active-source modeling: http://marineemlab.ucsd.edu/wham/

46. Salvus (open source full waveform modeling and inversion): https://salvus.io/

47. WingLink software (Schlumberger): http://www.slb.com/services/seismic/seismic-reservoir-characterization/electromagnetics/emsoftware/winglink.aspx

48. Tomographic full waveform inversion: https://sites.google.com/site/kscordua/downloads/tomographic-full-waveform-inversion

49. Escript: PED based geophysical modeling and inversion: http://iopscience.iop.org/article/10.1088/1742-2132/13/2/S59/meta

50. Rocks and Paleomagnetism http://magician.ucsd.edu/SIO247/

51. Essentials of Paleomagnetism https://earthref.org/MagIC/books/Tauxe/Essentials/

52. AGU GPE Teaching Materials: https://connect.agu.org/gpe/teaching-resources

53. PmagPy cookbook: https://earthref.org/PmagPy/cookbook/

54. PDE-based geophysical modelling using finite elements: examples from 3D resistivity and 2D magnetotellurics: https://academic.oup.com/jge/article/13/2/S59/5113414 

55. MT codes http://www.mtnet.info/main/source.html

56. 3D CSEM https://doi-org.ezproxy.lib.uh.edu/10.1190/geo2018-0208.1

57. Richard Blakely https://www.usgs.gov/staff-profiles/richard-blakely?logstash-usgs-pw%3Apalladium_root_topics=&logstash-usgs-pw%3Apalladium_root_publication_year_date=&sort=&page=2

58. UBC-GIF FEM1D and laterally constrained inversion: https://giftoolscookbook.readthedocs.io/en/latest/content/AtoZ/em1dfm/index.html#  

59. UBC-GIF TEM1D inPython: https://giftoolscookbook.readthedocs.io/en/latest/content/AtoZ/em1dtm/index.html# 

60. UBC-GIF MT/ZTEM inversion: https://giftoolscookbook.readthedocs.io/en/latest/content/AtoZ/NS/index.html 

61. SimPEG 2.5D DC inversion: https://docs.simpeg.xyz/content/tutorials/05-dcr/plot_inv_2_dcr2d.html

62. SimPEG linear magnetic inversion: http://computation.geosci.xyz/case-studies/PF/Linear_Problem_Mag.html

63. 2019 Geothermal Design Challenge: https://forge.pvgeo.org/project/gravity-inversion/01-gravity-mesh-refine.html

64. The MTPy software package for magnetotelluric data analysis and visualisation: https://github.com/MTgeophysics/mtpy 

65. Airborne electromagnetic systems: https://csegrecorder.com/articles/view/airborne-electromagnetic-systems-state-of-the-art-and-future-directions

66. Gravity for hydrocarbon exploration: https://www.geoexpro.com/articles/2016/04/gravity-for-hydrocarbon-exploration

67. Magnetics for hydrocarbon exploration: https://www.geoexpro.com/articles/2017/01/magnetics-for-hydrocarbon-exploration

68. Gravity gradiometry and its application in complex: https://www.geoexpro.com/articles/2011/01/looking-beyond-just-seismic-gravity-gradiometry-and-its-application-in-complex

69. 3D Time-domain EM modeling using SimPEG: https://github.com/sustechgem/SimPEG_Demo/tree/main/3D_TEM_FWD_Test

70. GeoApps developed by Mira Geoscience: https://geoapps.readthedocs.io/en/latest/; https://pypi.org/project/geoapps/

71. EM Webinar series: http://www.mtnet.info/webinars/webinars.html

72. pyGIMLi (an open-source multi-method library for modelling and inversion in geophysics): https://www.pygimli.org/

73. Australia Exploring for the Future Integrated Studies: https://www.ga.gov.au/eftf/minerals/fis/tennant-creek-mt-isa

74. Great presentation by Dr. Mark McLean on "3D inversion modelling of Full Spectrum FALCON® airborne gravity data over Otway Basin": https://www.youtube.com/watch?v=FwN9O1AnS3g 

75. GIFtoolsCookbook: https://giftoolscookbook.readthedocs.io/en/latest/content/AtoZ/AtoZ_index.html

76. SeisLib: A Python package for surface wave tomography: https://academic.oup.com/gji/advance-article-abstract/doi/10.1093/gji/ggac236/6613195

77.  Electromagnetic lecture series by Aleksander A. Kaufman: https://www.youtube.com/playlist?list=PLUfG7j4Lhdsf-qK9AZfyMMMO5JNy7Cdvd

78. Loop Structural: Open source 3D modeling library for creating 3D geological models: https://loop3d.github.io/LoopStructural/

Aleks:ander A

Mathematics and Computer Science

1. Linear algebra: Foundation to Frontiers: http://www.ulaff.net/;  http://wiki.cs.utexas.edu/rvdg/CalendarF2014

2. MA 580 Numerical analysis: http://mediasite.online.ncsu.edu/online/Catalog/catalogs/ma-580-kelley

3. CS 510 Image Computation https://www.cs.colostate.edu/~cs510/yr2013/progress.php

4. Support Vector Machine for Complex Outputs http://www.cs.cornell.edu/People/tj/svm_light/svm_struct.html

5. CS4780 Machine Learning http://machine-learning-course.joachims.org/

6. Support  Vector Machines at Microsoft Research http://research.microsoft.com/en-us/projects/svm/

7. Top publications in data mining http://academic.research.microsoft.com/RankList?entitytype=1&topDomainID=2&subDomainID=7&last=0&start=1&end=100

8. LSQR from Systems Optimization Laboratory http://web.stanford.edu/group/SOL/software/lsqr/

9. PSO http://clerc.maurice.free.fr/pso/

10. Most cited computerized medical imaging and graphics articles: http://www.journals.elsevier.com/computerized-medical-imaging-and-graphics/most-cited-articles/

11. Jing Qin (applied mathematics, optimization, compressive sensing): http://www.montana.edu/jqin/research.html

12. PSU Applied data mining and statistical learning: https://onlinecourses.science.psu.edu/stat857/intro

13. Yifei Lou  (applied math, compressive sensing, numerical optimization): https://sites.google.com/site/louyifei/

14. Matlab scripts for alternating direction method of multipliers: https://web.stanford.edu/~boyd/papers/admm/

15. Software for convex programming: http://cvxr.com/

16. Compressive sensing workshop http://people.ee.duke.edu/~lcarin/compressive-sensing-workshop.html

17. Kalman filter in a nutshell: https://towardsdatascience.com/kalman-filter-in-a-nutshell-e66154a06862

18. Leading Lesson: makes the problem solving process explicit for over a hundred multivariable calculus problems: http://www.leadinglesson.com/

19. Paul Hand: https://www.khoury.northeastern.edu/people/paul-hand/

 Resources on Python and Jupyter Notebook

1. Python for Earth Science students: https://nbviewer.jupyter.org/github/ltauxe/Python-for-Earth-Science-Students/blob/master/_TableOfContents.ipynb

2. A good introduction to Jupyter Notebook: https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook

3. Building interactive Dashboards with Jupyter: https://blog.dominodatalab.com/interactive-dashboards-in-jupyter/

4. An excellent Jupyter Notebook on matplotlib: http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

5. Scientific Python lectures by Johansson: https://github.com/jrjohansson/scientific-python-lectures

6. Earth Science related Python code: http://earthpy.org/

7. Jupyter Notebook: Interactive plot with widgets: https://stackoverflow.com/questions/44329068/jupyter-notebook-interactive-plot-with-widgets; https://blog.dominodatalab.com/interactive-dashboards-in-jupyter/; http://earthpy.org/pyncview_pm.html

8. Learning Python: from Zero to Hero: https://medium.freecodecamp.org/learning-python-from-zero-to-hero-120ea540b567 

9. Basic Plotting using Matplotlib: http://courses.csail.mit.edu/6.867/wiki/images/3/3f/Plot-python.pdf

10. Data Science Cheatsheets: https://www.datacamp.com/community/data-science-cheatsheets

11. Python loops tutorial: https://www.datacamp.com/community/tutorials/loops-python-tutorial

12. RISE: https://github.com/damianavila/RISE

13. Pandas tutorial: http://pandas.pydata.org/pandas-docs/version/0.15.2/tutorials.html

14. Panda tutorial: https://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/

15. Python data structure: https://www.datacamp.com/community/tutorials/data-structures-python

16. Jupyter Notebook cheetsheet: https://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed; https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet;  https://gist.github.com/pirafrank/07afdebf9ddc0704468f

17. Programming and Analysis of Geophysical data: http://olmozavala.com/index.php/courses/2016/programming-and-analysis-of-geophysical-data

18. Interactive controls in Jupyter Notebooks: https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6

19. Python tutorial: http://interactivepython.org/runestone/static/thinkcspy/index.html

20. Python for data analysis: http://www3.canisius.edu/~yany/python/Python4DataAnalysis.pdf

21. interactive, open-source, and browser-based graphing library for Python: https://github.com/plotly/plotly.py

22. Interactive widgets in Jupyter Notebook: https://ipython-books.github.io/33-mastering-widgets-in-the-jupyter-notebook/

23. Making GIF and short videos in Python: https://towardsdatascience.com/the-simplest-way-of-making-gifs-and-math-videos-with-python-aec41da74c6e

24. Load CSV to Google Colab: https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92

25. Embed an image into a Google Colab markdown cell: https://medium.com/analytics-vidhya/embedding-your-image-in-google-colab-markdown-3998d5ac2684

26. Seaborn for histograms: https://seaborn.pydata.org/generated/seaborn.histplot.html#seaborn.histplot

27. Learn Python from Harvard University: https://www.freecodecamp.org/news/learn-python-from-harvard-university/

Machine learning tutorials/articles

1. Deep learning history: https://medium.freecodecamp.org/the-history-of-deep-learning-explored-through-6-code-snippets-d0a0e8545202

2. A practical guide for coding deep learning: https://medium.freecodecamp.org/deep-learning-for-developers-tools-you-can-use-to-code-neural-networks-on-day-1-34c4435ae6b

3. A good high-level explanation of how Spotify’s Discover Weekly works (the image explaining collaborative filtering is so informative and self-explanatory!): https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe

4. Introduction to machine learning http://alex.smola.org/teaching/cmu2013-10-701x/

5. Google’s Teachable Machine: https://teachablemachine.withgoogle.com/, and a short article on it: https://www.engadget.com/2017/10/09/google-teachable-machine-learning-ai/

6. AlphaGo Zero: Learning from scratch: https://deepmind.com/blog/alphago-zero-learning-scratch/

7. Stanford Deep Learning Tutorial: http://ufldl.stanford.edu/tutorial/

8. Top 10 data mining algorithms: https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

9. Top algorithms/methods used by data scientists: https://www.kdnuggets.com/2016/09/poll-algorithms-used-data-scientists.html

10. Top 10 machine learning algorithms for beginners: https://www.kdnuggets.com/2017/10/top-10-machine-learning-algorithms-beginners.html/2

11. Deep learning specialization by Andrew Ng – 21 Lessons learned: https://medium.com/towards-data-science/deep-learning-specialization-by-andrew-ng-21-lessons-learned-15ffaaef627c

12. Engineering more reliable transportation with machine learning and AI at Uber: https://eng.uber.com/machine-learning/

13. Revolutionizing radiology with deep learning at Partners Healthcare: https://www.forbes.com/sites/tomdavenport/2017/11/05/revolutionizing-radiology-with-deep-learning-at-partners-healthcare-and-many-others/#19e81cc65e13

14. A new alternative to traditional neural network: https://www.technologyreview.com/the-download/609297/google-researchers-have-a-new-alternative-to-traditional-neural-networks/

15. Where to begin with neural nets: https://www.kdnuggets.com/2017/10/neural-networks-step-1.html

16. TensorFlow: Building Feed-forward neural networks step-by-step: https://www.kdnuggets.com/2017/10/tensorflow-building-feed-forward-neural-networks-step-by-step.html

17. Deep learning tutorial by Seth Weidman:  https://github.com/SethHWeidman/ODSC_Neural_Nets_11-04-17/blob/master/ODSC_Deep_Learning_2017.ipynb

18. Convolutional neural networks for visual recognition: http://cs231n.github.io/convolutional-networks/

19. Google Street View predicts politics: https://spectrum.ieee.org/cars-that-think/transportation/human-factors/deep-learning-and-google-street-view-can-predict-neighborhood-politics-from-parked-cars

20. Practical Machine learning tutorial with Python introduction: https://pythonprogramming.net/machine-learning-tutorial-python-introduction/

21. Install XGBoost on Windows https://www.ibm.com/developerworks/community/blogs/jfp/entry/Installing_XGBoost_For_Anaconda_on_Windows?lang=en

22. Build your first XGBoost model with scikit-learn: https://machinelearningmastery.com/develop-first-xgboost-model-python-scikit-learn/

23. Tree Boosting with XGBoost – Why does XGBoost win ‘every’ machine learning competition? https://medium.com/@Synced/tree-boosting-with-xgboost-why-does-xgboost-win-every-machine-learning-competition-ca8034c0b283

24. Introduction to gradient-boosted trees and XGBoost hyperparameter tuning (with Python): https://www.apprendimentoautomatico.it/en/introduction-to-gradient-boosted-trees-and-xgboost-hyperparameters-tuning-with-python/

25. Introduction to Deep learning for computer vision: http://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/

26. Stanford CS231n: Convolutional neural network for visual recognition: http://cs231n.stanford.edu/syllabus.html

27. 2017 was the year Google normalized machine learning: https://dgit.com/2017-year-google-normalized-machine-learning-53304/

28. 10 alarming predictions for deep learning in 2018: https://medium.com/intuitionmachine/10-fearless-predictions-for-deep-learning-in-2018-bc74a88b11d9

29. Documentary about Google’s DeepMind’s AlphaGo algorithm is now available on Netflix: http://www.businessinsider.com/the-documentary-about-google-deepminds-alphago-algorithm-is-now-available-on-netflix-2018-1

30. A year in computer vision: http://www.themtank.org/a-year-in-computer-vision

31. Introduction to Scikit-Learn: https://www.oreilly.com/ideas/intro-to-scikit-learn

32. Best Machine Learning libraries in Python: http://stackabuse.com/the-best-machine-learning-libraries-in-python/

33. NIPS2017: https://github.com/hindupuravinash/nips2017

34. Steepest descent in Tensorflow: http://copper.math.buffalo.edu/448/day23_s17.html

35. Gradient descent: https://am207.github.io/2017/wiki/gradientdescent.html

36. Gradient descent limitation: http://www.fredpark.com/blog

37. Gradient descent and backpropagation: http://www.deepideas.net/deep-learning-from-scratch-iv-gradient-descent-and-backpropagation/

38. Different version of gradient descent: http://ruder.io/optimizing-gradient-descent/

39. PCA and SVM applied to facial recognition: http://efavdb.com/machine-learning-for-facial-recognition-3/

40. Excellent explanation on gradient boosting: https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d

41. A history of machine translation from the Cold War to deep learning: https://medium.freecodecamp.org/a-history-of-machine-translation-from-the-cold-war-to-deep-learning-f1d335ce8b5

42. How AI can learn to generate pictures of cats: https://medium.freecodecamp.org/how-ai-can-learn-to-generate-pictures-of-cats-ba692cb6eae4

43. The building blocks of interpretability: https://distill.pub/2018/building-blocks/

44. Two minute papers on Youtube: https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg

45. Visualizing high-dimensional datasets using PCA and t-SNE in Python: https://medium.com/@luckylwk/visualising-high-dimensional-datasets-using-pca-and-t-sne-in-python-8ef87e7915b

46. t-SNE: https://lvdmaaten.github.io/tsne/ 

47. A guide for time series prediction using recurrent neural networks (LSTMs): https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f

48. Credit card fraud detection using Autoencoders in Keras: https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd

49. A great and friendly introduction to machine learning (strongly recommend; note that this is one of the series of A Friendly Introduction To XXX: https://www.youtube.com/watch?v=IpGxLWOIZy4

50. A visual introduction to machine learning (predicting house locations, NYC or SF): http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

51. The 7 steps of machine learning: https://www.youtube.com/watch?v=nKW8Ndu7Mjw

52. The state of AI by Andrew Ng: https://www.youtube.com/watch?v=NKpuX_yzdYs&t=407s

53. AI is the new electricity by Andrew Ng: https://www.youtube.com/watch?v=21EiKfQYZXc

54. Visualizing MNIST using dimensionality reduction: https://colah.github.io/posts/2014-10-Visualizing-MNIST/

55. PSU-Applied Multivariate Statistical Analysis:  interpretation of principal components: https://onlinecourses.science.psu.edu/stat505/node/54

56. PCA Procedures: https://www.theanalysisfactor.com/tips-principal-component-analysis/

57. How the backpropagation algorithm works: http://neuralnetworksanddeeplearning.com/chap2.html

58. Vanishing and exploding gradients: http://neuralnetworksanddeeplearning.com/chap5.html

59. Implementing batch normalization in TensorFlow: https://r2rt.com/implementing-batch-normalization-in-tensorflow.html

60. Learning rate: https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10

61. Only Numpy: Implementing simple ResNet: https://towardsdatascience.com/only-numpy-implementing-simple-resnet-for-mnist-classification-with-interactive-code-d58c77064304

62. Deep learning books notes: Chapter 1: https://becominghuman.ai/deep-learning-book-notes-chapter-1-b310837c76cf

63. Understanding batch normalization:  https://towardsdatascience.com/understanding-batch-normalization-with-examples-in-numpy-and-tensorflow-with-interactive-code-7f59bb126642

64. Only Numpy: Implementing GAN (general adversarial networks): https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021

65. Stochastic weight averaging: a new way to get state of the art results in deep learning: https://towardsdatascience.com/stochastic-weight-averaging-a-new-way-to-get-state-of-the-art-results-in-deep-learning-c639ccf36a

66. 5 Reasons Logistic Regression should be the first thing you learn when becoming a data scientist: https://towardsdatascience.com/5-reasons-logistic-regression-should-be-the-first-thing-you-learn-when-become-a-data-scientist-fcaae46605c4

67. How do I select SVM kernels? https://www.quora.com/How-do-I-select-SVM-kernels

68. Do people still use Python? https://www.quora.com/Do-people-still-use-Python

69. Recent advances for a better understanding of Deep learning https://towardsdatascience.com/recent-advances-for-a-better-understanding-of-deep-learning-part-i-5ce34d1cc914

70. Neuton: A new, disruptive neural network: https://www.zdnet.com/article/neuton-a-new-disruptive-neural-network-framework-for-ai-applications/

71. Free GPU on Google Colab: https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d

72. Getting to know Keras for new data scientists https://medium.com/@ODSC/getting-to-know-keras-for-new-data-scientists-cebbc42a3122

73. Logging in Tensorboard with PyTorch https://becominghuman.ai/logging-in-tensorboard-with-pytorch-or-any-other-library-c549163dee9e

74. Decision Trees – An Intuitive introduction: https://medium.com/x8-the-ai-community/decision-trees-an-intuitive-introduction-86c2b39c1a6c?cHa=true

75. Deep learning could reveal why the world works the way it does: https://medium.com/mit-technology-review/deep-learning-could-reveal-why-the-world-works-the-way-it-does-9be8b5fbfe4f

76. Generative AI: https://medium.com/cantors-paradise/generative-ai-a-key-to-machine-intelligence-674c89a81bc

77. Similarity and distance metrics for data science and machine learning: https://medium.com/dataseries/similarity-and-distance-metrics-for-data-science-and-machine-learning-e5121b3956f8

78. Understanding GAN: https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29 

79. Computer vision in Python https://www.pyimagesearch.com/

80. Setting up your PC/Workstation for Deep Learning: TensorFlow and PyTorch: https://towardsdatascience.com/setting-up-your-pc-workstation-for-deep-learning-tensorflow-and-pytorch-windows-9099b96035cb

81. Some reading materials on GANs: https://github.com/mayankgrwl97/gan-readings/blob/master/README.md

82. The state of machine learning frameworks in 2019: https://thegradient.pub/state-of-ml-frameworks-2019-pytorch-dominates-research-tensorflow-dominates-industry/

83. Deconvolutions and Checkerboard artefacts: https://distill.pub/2016/deconv-checkerboard/

84. Autoencoders: Overview of Research and Applications: https://towardsdatascience.com/autoencoders-overview-of-research-and-applications-86135f7c0d35

85. ML Glossary: loss functions https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html

86. From GAN to WGAN: https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html; https://zhuanlan.zhihu.com/p/25071913

87. Non-technical article on GAN: The GANFather: https://www.technologyreview.com/2018/02/21/145289/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/

88. A Tutorial on Information Maximizing Variational Autoencoders (InfoVAE): https://ermongroup.github.io/blog/a-tutorial-on-mmd-variational-autoencoders/

89. Leveraging Embeddings and Clustering Techniques in Computer Vision: https://blog.roboflow.com/embeddings-clustering-computer-vision-clip-umap/

90.  OpenAI CLIP: https://openai.com/research/clip

Writing

1. Best-kept secrets to winning proposals: http://www.nature.com/news/the-best-kept-secrets-to-winning-grants-1.22038

Geoscience without Borders

1. GWB Projects: http://seg.org/About-SEG/Geoscientists-Without-Borders/Projects

Latex

1. Latex Symbols: https://artofproblemsolving.com/wiki/index.php/LaTeX:Symbols

2. Pseduo-code in Latex: https://www.math-linux.com/latex-26/faq/latex-faq/article/how-to-write-algorithm-and-pseudocode-in-latex-usepackage-algorithm-usepackage-algorithmic

3. Use colors in Latex: https://www.overleaf.com/learn/latex/Using_colours_in_LaTeX

Education

1. Computer Science falling behind: https://www.insidehighered.com/news/2017/10/27/even-booms-student-enrollment-not-enough-degrees-keep-jobs-computer-science

2. Don't be a prig in peer review: https://www.nature.com/articles/d41586-020-02512-0

Miscellaneous

1. Data analysis in the Geosciences: http://strata.uga.edu/8370/index.html

2. Stand Up for Best Practices: Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper https://towardsdatascience.com/stand-up-for-best-practices-8a8433d3e0e8

3. Removing Personal Information from Adobe PDF and Microsoft Word files http://www.siam.org/journals/identity.php

4. Fortran 90/95 reference http://www.icl.utk.edu/~mgates3/docs/fortran.html

5. RGB to HEX: https://www.rgbtohex.net/rgb/ 

6. Color encyclopedia: https://www.colorhexa.com/

7. File converter (e.g., convert m4a to mp3): https://cloudconvert.com/

8. Make your Google site searchable by search engine: https://support.google.com/sites/thread/25156942?hl=en; https://www.steegle.com/google-sites/how-to/verify-search-console?utm_source=sites-help-forum&utm_medium=post&utm_campaign=new-sites

9. Interpolation on regular grid: https://www.mathworks.com/help/matlab/ref/interp2.html

10. Interpolating scattered data: https://www.mathworks.com/help/matlab/math/interpolating-scattered-data.html

Art

1, Create your own image using machine learning (this one is fun, you should try it!): https://www.artbreeder.com/

2. Create your geology model in a fun way: https://app.visiblegeology.com/profile.html

3. Drawn to Science: https://blogs.agu.org/sciencecommunication/2020/08/05/drawntogeoscience-imaginations-to-animations/ 

4. AI won an art contest, and artists are furious: https://www.cnn.com/2022/09/03/tech/ai-art-fair-winner-controversy/index.html