- BCI Events - A list of BCI related conferences summarized by Dr.Fabien Lotte. Very useful.
- Wait But Why, Neuralink and the Brain's Magical Future - A very good blog covering from the very basics of how neuron works to the concepts of brain machine interface and where's the Neuralink company is heading towards.
- Makoto's preprocessing pipeline - Very detailed step by step instructions and reasoning on EEG cleaning pipeline. Although these steps could not simply be applied to your data since there's always things to account for, it's a good source to understand basic steps in EEG processing. The codes is also available.
- Online EEGLAB workshop - An awesome lists of resources to understand methods regarding EEG. It has videos, pdfs, articles you could refer to.
- EEGLAB list archives - Not always useful, but could be a good source to find a specific answer regarding EEG. If you want to find out more than two words at the same time, Google chrome extension: Multi-highlight could be useful.
- EEGLAB tutorials - All the tutorials on EEGLAB are really useful when understanding EEG related things. But they are also a little specific to EEGLAB open source library.
- Fieldtrip tutorials - All the tutorials on Fieldtrip are very useful when understanding EEG related or fMRI related things.
- Brainstorm tutorials - Brainstorm tutorials are well written and often useful even if you are not using the software. Especially useful when using individual MRI combined with EEG.
- Analyzing Neural Time Series Data: Theory and Practice - A very good book to understand analysis on EEG. Covers from the basics of how to use MATLAB to in depth time frequency analysis in EEG.
- Neuroscience online - A detailed web articles talking about basics in neuroscience.
- Learn Medical Neuroscience - A portal where tons of links are collected to learn about medical neuroscience. Recommended by a coursera course: Medical Neuroscience.
- DSP Guide - A detailed reference for DSP. All the pdfs are available here.
- Essence of linear algebra - Very good videos with full or visualizations to understand the intuitive meaning of linear algebras.
- Pattern recognition and Machine learning - A good book for referencing each of the content taught in the book (basic information theory for entropy to mutual information, machine learning algorithms such as linear discriminant analysis to SVM).
- Seeing theory - For visually understanding basics in probability and statistics.
- Flowchart for choosing the right statistical tests - A good flowchart that summarizes various statistical tests and let you choose the right one for your analysis. However, there's no explanation about each methods.
- Algorithm visualizer - Visualizes basic alrogithms with codes by the side. Good to understand some basic algorithms if you don't know them.
- Algorithms, Part 1 - Haven't tried it myself, but seems to be good according to my friends.
- Machine learning by Andrew Ng - A very good course about machine learning. Starts from the elemental foundations to semi advanced level in machine learning.
- Neural Network Architectures - A good blog that talks about a history of CNN. In the end it talks optimized method called ENet (Efficient Net).
- Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize? - A blog talking about why Stochastic Gradient Descent (SGD) is so good at generalizing.
- The Best Sources to Study Machine Learning and AI: Quora Session Highlight | Ben Hamner, Kaggle CTO - How you should learn machine learning: 1) Find problems that interest you, 2) Find the simplest way to solve it, 3) Brush it up and make it open source
- Awesome - Most Cited Deep Learning Papers - A list of most cited deep learning papers.
- Hyperparameter optimization for Neural Networks - A good article talking about various hyper parameter tuning in neural networks.
- Foundations for deep learning - A good resources to learn deep learning from information theory, mathematics, neuroscience, and statistical physics. I especially like the beginning part of this repo's readme: "I emphasize mathematical/conceptual foundations because implementations of ideas(ex. Torch, Tensorflow) will keep evolving but the underlying theory must be sound. Anybody with an interest in deep learning can and should try to understand why things work".
- Implementing Convolutional Neural Networks - A good blog explaining about the basics of how the CNN works in its core (Convolution layer, Maxpool layer, Batchnorm layer, etc).
- The Limitations of Deep Learning - It's always good to know what's the advantages and disadvantages of something. "In general, anything that requires reasoning—like programming, or applying the scientific method—long-term planning, and algorithmic-like data manipulation, is out of reach for deep learning models, no matter how much data you throw at them. Even learning a sorting algorithm with a deep neural network is tremendously difficult."
- Yes, you should understand back propagation - A good blog post to understand backpropagation algorithm.
- An overview of gradient descent optimization algorithms - A very good reference blog talking about all sorts of gradient descent optimization methods.
- Optimization for Deep Learning Highlights in 2017 - A blog post summarizing optimization methods for deep learning and also about tuning parameters.
- Understanding Kalman Filters - A good videos talking about the very basics of kalman filters to unscented kalman filter and particle filters in the end. Good for getting the feel of it.
- How a Kalman filter works, in pictures - Very detailed step by step instructions on how a normal kalman filter works with figures. Good for understanding the ordinary kalman filter.
- MATLAB plot gallery - A very good source for plotting in MATLAB provided by Mathworks. Each figure has a link to the source so it's useful to find the plots you want to plot and make one yourself.
- Github totorial - An interactive website to learn how to use git shell. Recommended if you are starting to use git.
- Writing a friendly README - Talks about how to write a good README for a git repository.
- Kanbanflow - A good way to manage different projects.
- Overleaf - Online Latex writing service for papers and other documents. There's no need for you to compile and import libraries to write documents in Latex.
- Writing academic papers in plain text with markdown and jupyter notebook - A blog post talking about how to use Markdown style text editing and jupyter notebook to write a journal paper. Pandoc explained in here looks useful when converting a plain text in markdown format to Latex or other styles.
- Some modest advice for Graduate Students - A little old, but a good list of advice for Ph.D. students.
- A research to engineering workflow - A good blog talking about tips about workflows. More suitable for engineers especially working in the machine learning fields.
- How can a graduate student make the best out of his/her PhD experience? - "Finally, do not be intimidated by the amount of reading you will be doing. This is almost the last time in your life when you will have the luxury of having the time to read. It is a tremendous gift to you. Take advantage of it."
- How to Be a Successful PhD Student - Some implications for PhD student. More for software engineering student.