A basic intro to stats for neuroscientists and all course materials are open here
Jupyter notebook slides & RISE with code to play around with to build an intuition for stats...
#neuroscience
#course
Jupyter notebook slides & RISE with code to play around with to build an intuition for stats...
#neuroscience
#course
GitHub
GitHub - BlohmLab/NSCI801-QuantNeuro: NSCI 801 (Queen's U) Quantitative Neuroscience course materials
NSCI 801 (Queen's U) Quantitative Neuroscience course materials - GitHub - BlohmLab/NSCI801-QuantNeuro: NSCI 801 (Queen's U) Quantitative Neuroscience course materials
Machine learning in Python with scikit-learn
Ref. 41026
Duration: 8 weeks
Effort: 35 hours
Pace: ~4h15/week
Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!
#ML
#scikit_learn
#course
Ref. 41026
Duration: 8 weeks
Effort: 35 hours
Pace: ~4h15/week
Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!
#ML
#scikit_learn
#course
FUN MOOC
Machine learning in Python with scikit-learn
Build predictive models with scikit-learn and gain a practical understanding of the strengths and limitations of machine learning!
Neural Networks from Scratch in Python
By Harrison Kinsley
Book
https://nnfs.io/
And his YouTube series.
"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.
Github
#course
#book
By Harrison Kinsley
Book
https://nnfs.io/
And his YouTube series.
"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.
Github
#course
#book
YouTube
Neural Networks from Scratch in Python
Share your videos with friends, family, and the world
UVA DEEP LEARNING COURSE
MSc in Artificial Intelligence for the University of Amsterdam.
https://uvadlc.github.io/
GitHub
#course
#DL
MSc in Artificial Intelligence for the University of Amsterdam.
https://uvadlc.github.io/
GitHub
#course
#DL
GitHub
uvadlc_notebooks/docs/tutorial_notebooks at master · phlippe/uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023 - phlippe/uvadlc_notebooks
scikit-learn course
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.
The course description can be found here.
GitHub
#course
#MachineLearning
The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background.
The course description can be found here.
GitHub
#course
#MachineLearning
GitHub
GitHub - INRIA/scikit-learn-mooc: Machine learning in Python with scikit-learn MOOC
Machine learning in Python with scikit-learn MOOC. Contribute to INRIA/scikit-learn-mooc development by creating an account on GitHub.
Introduction to Deep Learning
-- 170 Video Lectures from Adaptive Linear Neurons to Zero-shot Classification with Transformers
Jul 9, 2021
by Sebastian Raschka
#course
#DL
-- 170 Video Lectures from Adaptive Linear Neurons to Zero-shot Classification with Transformers
Jul 9, 2021
by Sebastian Raschka
#course
#DL
Sebastian Raschka, PhD
Introduction to Deep Learning
I just sat down this morning and organized all deep learning related videos I recorded in 2021. I am sure this will be a useful reference for my future self,...
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