Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
http://databookuw.com/
Matlab and Python codes available.
Video lectures by authors available on YouTube.
#book
#course
#ML
Steven L. Brunton, J. Nathan Kutz
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
http://databookuw.com/
Matlab and Python codes available.
Video lectures by authors available on YouTube.
#book
#course
#ML
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Montbrio Dashboard
Montbrió, E., Pazó, D. and Roxin, A., 2015. Macroscopic description for networks of spiking neurons. Physical Review X, 5(2), p.021028.
GitHub
t.me/scientific_programming
Montbrió, E., Pazó, D. and Roxin, A., 2015. Macroscopic description for networks of spiking neurons. Physical Review X, 5(2), p.021028.
GitHub
t.me/scientific_programming
JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research.
With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy code. It can differentiate through a large subset of Python’s features, including loops, ifs, recursion, and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
Link
With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy code. It can differentiate through a large subset of Python’s features, including loops, ifs, recursion, and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.
Link
بازسازی کیفیت تصاویر قدیمی با یادگیری عمیق!
معماری GFP-GAN یک مدل هوش مصنوعی رایگان برای ترمیم عکس است.
GFPGAN aims at developing Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration.
GitHub
معماری GFP-GAN یک مدل هوش مصنوعی رایگان برای ترمیم عکس است.
GFPGAN aims at developing Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration.
GitHub
Statistics and Machine Learning in
Python.pdf
Python.pdf
11.9 MB
Forwarded from the Turing Machine
Case Studies in Neural Data Analysis
Author: Mark Kramer and Uri Eden
This repository is a companion to the textbook Case Studies in Neural Data Analysis, by Mark Kramer and Uri Eden. That textbook uses MATLAB to analyze examples of neuronal data. The material here is similar, except that we use Python.
[ link ] [ git ] [ book ]
Follow: @theTuringMachine
Author: Mark Kramer and Uri Eden
This repository is a companion to the textbook Case Studies in Neural Data Analysis, by Mark Kramer and Uri Eden. That textbook uses MATLAB to analyze examples of neuronal data. The material here is similar, except that we use Python.
[ link ] [ git ] [ book ]
Follow: @theTuringMachine
8th Workshop on Collaborative Scientific Software Development and Management of Open Source Scientific Packages
Writing software has become central to research in many fields of science. This Workshop aims to give early-career scientists an introduction to a variety of topics that help them to write efficient, clean, maintainable and long-lived code that is useful beyond solving an immediate problem. In a mixture of talks and many hands-on sessions, the focus lies on showing best practices and building fundamental skills in creating, extending and collaborating on modular and reusable software.
Topics:
Effective collaborative development with multiple co-authors
Python / shell scripts as glue code
Modular, reusable software design
Software optimization
Version control and release cycles
Automated testing frameworks
Structured documentation
Management of open source scientific packages
Continuous integration & deployment
Conversational development
The online Workshop has a limited number of 35 seats. Participation requires attending all e-classes and a full-time commitment to the final 3-days projects, in which participants will work in remote groups. At the end of the course, to obtain the final certificates, group members are expected to leverage on collaborative development techniques to deliver a final software.
Deadline for applications is set at 27th September, participants can apply here: https://e-applications.ictp.it/applicant/login/3646
For more information please see the Workshop website: http://indico.ictp.it/event/9700/
Ivan
Writing software has become central to research in many fields of science. This Workshop aims to give early-career scientists an introduction to a variety of topics that help them to write efficient, clean, maintainable and long-lived code that is useful beyond solving an immediate problem. In a mixture of talks and many hands-on sessions, the focus lies on showing best practices and building fundamental skills in creating, extending and collaborating on modular and reusable software.
Topics:
Effective collaborative development with multiple co-authors
Python / shell scripts as glue code
Modular, reusable software design
Software optimization
Version control and release cycles
Automated testing frameworks
Structured documentation
Management of open source scientific packages
Continuous integration & deployment
Conversational development
The online Workshop has a limited number of 35 seats. Participation requires attending all e-classes and a full-time commitment to the final 3-days projects, in which participants will work in remote groups. At the end of the course, to obtain the final certificates, group members are expected to leverage on collaborative development techniques to deliver a final software.
Deadline for applications is set at 27th September, participants can apply here: https://e-applications.ictp.it/applicant/login/3646
For more information please see the Workshop website: http://indico.ictp.it/event/9700/
Ivan
Indico - Conferences and Events
8th Workshop on Collaborative Scientific Software Development and Management of Open Source Scientific Packages | (smr 3646) (19…
An ICTP Virtual Meeting
Writing software has become central to research in many fields of science. This school aims to give early-career scientists an introduction to a variety of topics that help them to write efficient, clean, maintainable and long-lived…
Writing software has become central to research in many fields of science. This school aims to give early-career scientists an introduction to a variety of topics that help them to write efficient, clean, maintainable and long-lived…
Postdoc Position Available
High Performance and Cloud Computing
https://www.ictp.it/about-ictp/media-centre/news/2021/8/postdoc_icts.aspx#close
ICTP seeks applications for a postdoctoral position starting January 2022 from outstanding young scientists with a strong research record. ICTP is a UNESCO Category 1 research institute supporting science and education in the developing world. ICTP promotes worldwide initiatives for the career development of women in science.
https://e-applications.ictp.it/applicant/login/IT22
Application deadline: 19 November 2021
#postdoc
#position
High Performance and Cloud Computing
https://www.ictp.it/about-ictp/media-centre/news/2021/8/postdoc_icts.aspx#close
ICTP seeks applications for a postdoctoral position starting January 2022 from outstanding young scientists with a strong research record. ICTP is a UNESCO Category 1 research institute supporting science and education in the developing world. ICTP promotes worldwide initiatives for the career development of women in science.
https://e-applications.ictp.it/applicant/login/IT22
Application deadline: 19 November 2021
#postdoc
#position
Statistics with Julia is your one stop shop for statistics, machine learning, and data science using the Julia language. Use the book if you are a Julia user who wants to learn statistics or improve your statistics knowledge. Use it if you know some statistics and want to explore how it is done via Julia. Use it if you are just entering the world of data science and want to pick up a cool modern programming language together with the study of elementary statistical concepts needed for machine learning, data science, and artificial intelligence. You can read it cover to cover, or access bits in random order. Most importantly you may try out the Julia source code as you read the book.
https://statisticswithjulia.org/
https://statisticswithjulia.org/
Python tutorial (persian)
معرفی درس و هدف درس
هدف از درس برنامه سازی (پایتون) توسعه و تعمیق دانش و مهارت دانشجویان در زمینه تفکر سیستمی، آلگوریتم و برنامه نویسی در محیط پایتون است. اهداف اصلی در این درس عبارتند از:
آشنائی با مبانی برنامه نویسی و ساختارهای برنامه سازی در پایتون
آشنائی با توابع و ساختارهای پیشرفته از جمله لیست، دیکشنری، کلاس و آبجکت و ساختار توابع بازگشتی
آشنائی با برنامه نویسی شی گرا، وراثت، برنامه های مرتب سازی و جستجو و مفهوم پیچیدگی محاسباتی
آشنائی با کتابخانه های پایتون Python, Numpy, Matplotlib …
پیاده سازی و کسب مهارت انجام پروژههای درسی
GitHub
معرفی درس و هدف درس
هدف از درس برنامه سازی (پایتون) توسعه و تعمیق دانش و مهارت دانشجویان در زمینه تفکر سیستمی، آلگوریتم و برنامه نویسی در محیط پایتون است. اهداف اصلی در این درس عبارتند از:
آشنائی با مبانی برنامه نویسی و ساختارهای برنامه سازی در پایتون
آشنائی با توابع و ساختارهای پیشرفته از جمله لیست، دیکشنری، کلاس و آبجکت و ساختار توابع بازگشتی
آشنائی با برنامه نویسی شی گرا، وراثت، برنامه های مرتب سازی و جستجو و مفهوم پیچیدگی محاسباتی
آشنائی با کتابخانه های پایتون Python, Numpy, Matplotlib …
پیاده سازی و کسب مهارت انجام پروژههای درسی
GitHub
GitHub
GitHub - NiloofarMn/python_tutorial_in_persian: در این ریپازیتوری میتوانید مطالب اولیه پایتون را بیاموزید.
در این ریپازیتوری میتوانید مطالب اولیه پایتون را بیاموزید. - NiloofarMn/python_tutorial_in_persian
Simulation based Inference for scientific discovery workshop 2021.
You use simulation in physics, economics, archaeology and want to find the simulator parameters that best fits the observations.
https://mlcolab.org/sbi-workshop
You use simulation in physics, economics, archaeology and want to find the simulator parameters that best fits the observations.
https://mlcolab.org/sbi-workshop
PhD Candidate for Numerical and Computational Analysis for Mathematical Neuroscience
Apply
#PhD
#position
Apply
#PhD
#position
IamExpat
Research / Academic jobs in Nijmegen | IamExpat Jobs
Vacancies and jobs in Research / Academic in English for expats in Nijmegen. Find jobs by recruiters and international companies.
Super Study Guide Data Science Tools.pdf
2.8 MB
Quick guide
SQL, R, Python
SQL, R, Python