The Roles of Supervised Machine Learning in Systems Neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML’s contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
https://arxiv.org/ftp/arxiv/papers/1805/1805.08239.pdf
#neuroscience #machine_learning
An Improved EM algorithm
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM)). Our experiment with expectation maximization is performed in the following three cases: initialize randomly; initialize with result of K-means; initialize with result of K-medoids. The experiment result shows that expectation maximization algorithm depend on its initial state or parameters. And we found that EM initialized with K-medoids performed better than both the one initialized with K-means and the one initialized randomly.
https://arxiv.org/abs/1305.0626
#machine_learning #statistics
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM)). Our experiment with expectation maximization is performed in the following three cases: initialize randomly; initialize with result of K-means; initialize with result of K-medoids. The experiment result shows that expectation maximization algorithm depend on its initial state or parameters. And we found that EM initialized with K-medoids performed better than both the one initialized with K-means and the one initialized randomly.
https://arxiv.org/abs/1305.0626
#machine_learning #statistics
arXiv.org
An Improved EM algorithm
In this paper, we firstly give a brief introduction of expectation
maximization (EM) algorithm, and then discuss the initial value sensitivity of
expectation maximization algorithm. Subsequently,...
maximization (EM) algorithm, and then discuss the initial value sensitivity of
expectation maximization algorithm. Subsequently,...
A great course about machine learning
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLEBC422EC5973B4D8
#machine_learning
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLEBC422EC5973B4D8
#machine_learning
YouTube
Lecture 1 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.
This course provides a broad introduction to machine learning and…
This course provides a broad introduction to machine learning and…
A friendly introduction to machine learning
It is a great video for people who want to start learning machine learning, but they don't know a lot about it. So, it will help them to find a good insight into this field.
https://www.youtube.com/watch?v=IpGxLWOIZy4
#machine_learning
It is a great video for people who want to start learning machine learning, but they don't know a lot about it. So, it will help them to find a good insight into this field.
https://www.youtube.com/watch?v=IpGxLWOIZy4
#machine_learning
YouTube
A Friendly Introduction to Machine Learning
Grokking Machine Learning Book: https://www.manning.com/books/grokking-machine-learning
40% discount promo code: serranoyt
A friendly introduction to the main algorithms of Machine Learning with examples.
No previous knowledge required.
What is Machine…
40% discount promo code: serranoyt
A friendly introduction to the main algorithms of Machine Learning with examples.
No previous knowledge required.
What is Machine…
Forwarded from Tensorflow(@CVision) (Amir Mohammad Ghoreyshi)
نمایش بصری گراف معماری شبکه های عصبی یادگیری عمیق و یادگیری ماشین
Visualizer for neural network, deep learning and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models.
Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb, predict_net.pbtxt), MXNet (.model, -symbol.json), TorchScript (.pt, .pth), NCNN (.param) and TensorFlow Lite (.tflite).
Netron has experimental support for PyTorch (.pt, .pth), Torch (.t7), CNTK (.model, .cntk), Deeplearning4j (.zip), PaddlePaddle (.zip, model), Darknet (.cfg), scikit-learn (.pkl), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt).
https://github.com/lutzroeder/netron
Visualizer for neural network, deep learning and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models.
Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb, predict_net.pbtxt), MXNet (.model, -symbol.json), TorchScript (.pt, .pth), NCNN (.param) and TensorFlow Lite (.tflite).
Netron has experimental support for PyTorch (.pt, .pth), Torch (.t7), CNTK (.model, .cntk), Deeplearning4j (.zip), PaddlePaddle (.zip, model), Darknet (.cfg), scikit-learn (.pkl), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt).
https://github.com/lutzroeder/netron
GitHub
GitHub - lutzroeder/netron: Visualizer for neural network, deep learning and machine learning models
Visualizer for neural network, deep learning and machine learning models - lutzroeder/netron
A comprehensive list of Neural Architecture Search (NAS) related papers
https://www.automl.org/automl/literature-on-neural-architecture-search/
#machine_learning
https://www.automl.org/automl/literature-on-neural-architecture-search/
#machine_learning
Forwarded from Deep learning channel (Mohsen Fayyaz)
Jeremy Jordan
Organizing machine learning projects: project management guidelines.
The goal of this document is to provide a common framework for approaching machine learning projects that can be referenced by practitioners. If you build ML models, this post is for you.
Machine Learning for Physics and the Physics of Learning Tutorials
https://t.co/4nfcnWkQtM?amp=1
#machine_learning #physics
https://t.co/4nfcnWkQtM?amp=1
#machine_learning #physics
IPAM
Machine Learning for Physics and the Physics of Learning Tutorials (Schedule) - IPAM
New Educational Methods for Learning Mathematics
https://www.youtube.com/watch?v=X_CK1e0Lmxw
#mathematics
https://www.youtube.com/watch?v=X_CK1e0Lmxw
#mathematics
YouTube
Common Core Math Explained
Dr. Raj Shah, owner and founder of Math Plus Academy (mathplusacademy.com) explains why math is taught differently than it was in the past and helps address parents' misconceptions about the "new math". Original video: https://vimeo.com/110807219
François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.
https://www.youtube.com/watch?v=Bo8MY4JpiXE&t=173s
#machine_learning #artificial_intelligence #podcast
https://www.youtube.com/watch?v=Bo8MY4JpiXE&t=173s
#machine_learning #artificial_intelligence #podcast
YouTube
François Chollet: Keras, Deep Learning, and the Progress of AI | Artificial Intelligence Podcast
François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with d...
A great and comprehensive review of meta-learning algorithms
https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
#meta_learning #deep_learning #machine_learning
https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
#meta_learning #deep_learning #machine_learning
Lil'Log
Meta Learning
The purpose of this handout is to help you use statistics to make your argument as effectively as possible, especially in your research paper.
https://writingcenter.unc.edu/tips-and-tools/statistics/
https://writingcenter.unc.edu/tips-and-tools/statistics/
The Writing Center • University of North Carolina at Chapel Hill
Statistics – The Writing Center • University of North Carolina at Chapel Hill
There are lies, damned lies, and statistics. —Mark Twain What this handout is about The purpose of this handout is to help you use statistics to make your argument as effectively as possible. Introduction Numbers are power. Apparently freed of … Read more
Genetic Neural Architecture Search.pdf
1.5 MB
Today I published the pre-print version of my first paper which is about designing a new evolutionary algorithm for neural architecture search in Arxiv.
https://arxiv.org/abs/1909.09432
https://arxiv.org/abs/1909.09432