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paper.bib
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@book{becker_carpentries_nodate,
title = {The {Carpentries} {Curriculum} {Development} {Handbook}},
url = {https://cdh.carpentries.org/},
abstract = {This is a work in progress of the curriculum development handbook for The Carpentries.},
urldate = {2023-09-01},
author = {Becker, Erin and Michonneau, François},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/VHBDXGBU/cdh.carpentries.org.html:text/html},
}
@misc{noauthor_carpentries_nodate,
title = {The {Carpentries} {Workbench}},
url = {https://carpentries.github.io/workbench/},
urldate = {2023-09-01},
file = {The Carpentries Workbench:/Users/svenvanderburg/Zotero/storage/SSBZ6XPS/workbench.html:text/html},
}
@misc{noauthor_fastai_nodate,
title = {fast.ai - {Practical} {Deep} {Learning} for {Coders}},
url = {https://course.fast.ai/},
abstract = {A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.},
language = {en},
urldate = {2023-09-01},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/CINJVAEW/course.fast.ai.html:text/html},
}
@misc{noauthor_udemy_nodate,
title = {Udemy - {Basics} of {Deep} {Learning}},
url = {https://www.udemy.com/course/basics-of-deep-learning/},
abstract = {Fundamentals of Neural Network - Free Course},
language = {en-us},
urldate = {2023-09-01},
journal = {Udemy},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/L57M3IZP/basics-of-deep-learning.html:text/html},
}
@misc{noauthor_udemy_nodate-1,
title = {Udemy - {Tensorflow} 2.0 {\textbar} {Recurrent} {Neural} {Networks}, {LSTMs}, {GRUs}},
url = {https://www.udemy.com/course/tensorflow-20-recurrent-neural-networks-lstms-grus/},
abstract = {Sequence prediction course that covers topics such as: RNN, LSTM, GRU, NLP, Seq2Seq, Attention, Time series prediction - Free Course},
language = {en-us},
urldate = {2023-09-01},
journal = {Udemy},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/GNQFE2IZ/tensorflow-20-recurrent-neural-networks-lstms-grus.html:text/html},
}
@misc{noauthor_udemy_nodate-2,
title = {Udemy - {Data} {Science}: {Intro} {To} {Deep} {Learning} {With} {Python}},
shorttitle = {Free {Deep} {Learning} {Tutorial} - {Data} {Science}},
url = {https://www.udemy.com/course/complete-deep-learning-course-with-python/},
abstract = {Learn to create Deep Learning Algorithms in Python - Free Course},
language = {en-us},
urldate = {2023-09-01},
journal = {Udemy},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/EAUMLMBT/complete-deep-learning-course-with-python.html:text/html},
}
@misc{noauthor_coursera_nodate,
title = {Coursera - {Deep} {Learning}},
url = {https://www.coursera.org/specializations/deep-learning},
abstract = {Learn Deep Learning from deeplearning.ai. If you want to break into Artificial intelligence (AI), this Specialization will help you. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.},
language = {en},
urldate = {2023-09-01},
journal = {Coursera},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/UGSGGSTG/deep-learning.html:text/html},
}
@misc{noauthor_freecodecamporg_2022,
title = {{freeCodeCamp}.org - {Learn} {PyTorch} for {Deep} {Learning}},
url = {https://www.freecodecamp.org/news/learn-pytorch-for-deep-learning-in-day/},
abstract = {My comprehensive PyTorch course is now live on the freeCodeCamp.org YouTube channel. * You can view the full 26 hour course here [https://youtu.be/V\_xro1bcAuA]. * Read the course materials online for free at learnpytorch.io [https://learnpytorch.io/]. * See all of the course materials on GitHub},
language = {en},
urldate = {2023-09-01},
journal = {freeCodeCamp.org},
month = oct,
year = {2022},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/FRRJZMVA/learn-pytorch-for-deep-learning-in-day.html:text/html},
}
@misc{noauthor_csc-_nodate,
title = {{CSC}- {Practical} {Deep} {Learning}},
url = {https://ssl.eventilla.com/event/8aPek},
abstract = {Eventilla},
language = {en},
urldate = {2023-09-01},
journal = {Eventilla},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/HLHX5EKN/8aPek.html:text/html},
}
@article{wilson_software_2006,
title = {Software {Carpentry}: {Getting} {Scientists} to {Write} {Better} {Code} by {Making} {Them} {More} {Productive}},
volume = {8},
issn = {1558-366X},
shorttitle = {Software {Carpentry}},
doi = {10.1109/MCSE.2006.122},
abstract = {For the past years, my colleagues and I have developed a one-semester course that teaches scientists and engineers the "common core" of modern software development. Our experience shows that an investment of 150 hours-25 of lectures and the rest of practical work-can improve productivity by roughly 20 percent. That's one day a week, one less semester in a master's degree, or one less year for a typical PhD. The course is called software carpentry, rather than software engineering, to emphasize the fact that it focuses on small-scale and immediately practical issues. All of the material is freely available under an open-source license at www.swc.scipy.org and can be used both for self-study and in the classroom. This article describes what the course contains, and why},
number = {6},
journal = {Computing in Science \& Engineering},
author = {Wilson, G.},
month = nov,
year = {2006},
note = {Conference Name: Computing in Science \& Engineering},
keywords = {computation in undergraduate education, Computer science, continuing education, Debugging, Ethics, Java, Open source software, Physics, physics education, Portable computers, Programming profession, software engineering, Teamwork, World Wide Web},
pages = {66--69},
file = {IEEE Xplore Abstract Record:/Users/svenvanderburg/Zotero/storage/AU7X84AP/1717319.html:text/html},
}
@book{lang_small_2021,
title = {Small {Teaching}: {Everyday} {Lessons} from the {Science} of {Learning}},
isbn = {978-1-119-75554-8},
shorttitle = {Small {Teaching}},
abstract = {A freshly updated edition featuring research-based teaching techniques that faculty in any discipline can easily implement Research into how we learn can help facilitate better student learning—if we know how to apply it. Small Teaching fills the gap in higher education literature between the primary research in cognitive theory and the classroom environment. In this book, James Lang presents a strategy for improving student learning with a series of small but powerful changes that make a big difference―many of which can be put into practice in a single class period. These are simple interventions that can be integrated into pre-existing techniques, along with clear descriptions of how to do so. Inside, you’ll find brief classroom or online learning activities, one-time interventions, and small modifications in course design or student communication. These small tweaks will bring your classroom into alignment with the latest evidence in cognitive research. Each chapter introduces a basic concept in cognitive research that has implications for classroom teaching, explains the rationale for offering it within a specific time period in a typical class, and then provides concrete examples of how this intervention has been used or could be used by faculty in a variety of disciplines. The second edition features revised and updated content including a newly authored preface, new examples and techniques, updated research, and updated resources. How can you make small tweaks to your teaching to bring the latest cognitive science into the classroom? How can you help students become good at retrieving knowledge from memory? How does making predictions now help us learn in the future? How can you build community in the classroom? Higher education faculty and administrators, as well as K-12 teachers and teacher trainers, will love the easy-to-implement, evidence-based techniques in Small Teaching.},
language = {en},
publisher = {John Wiley \& Sons},
author = {Lang, James M.},
month = aug,
year = {2021},
note = {Google-Books-ID: k8E4EAAAQBAJ},
keywords = {Education / General, Education / Learning Styles, Education / Schools / Levels / Higher, Education / Teaching / General},
}
@misc{azalee_bostroem_software_2016,
title = {Software {Carpentry}: {Programming} with {Python}.},
url = {https://github.com/swcarpentry/python-novice-inflammation, 10.5281/zenodo.57492},
abstract = {Programming with Python. Contribute to swcarpentry/python-novice-inflammation development by creating an account on GitHub.},
language = {en},
urldate = {2023-09-01},
journal = {GitHub},
author = {{Azalee Bostroem} and {Trevor Bekolay} and {Valentina Staneva (eds)}},
month = jun,
year = {2016},
note = {Version 2016.06},
file = {Snapshot:/Users/svenvanderburg/Zotero/storage/227MJWAZ/CITATION.html:text/html},
}
@misc{noauthor_scikit-learn_2023,
title = {scikit-learn course},
copyright = {CC-BY-4.0},
url = {https://github.com/INRIA/scikit-learn-mooc},
abstract = {Machine learning in Python with scikit-learn MOOC},
urldate = {2023-09-01},
publisher = {Inria},
month = sep,
year = {2023},
note = {original-date: 2020-03-09T14:53:36Z},
keywords = {machine-learning, mooc, python, scikit-learn},
}