From e9d6c9f194f3999f27b7156b871945443ce74ce9 Mon Sep 17 00:00:00 2001 From: tapyu Date: Tue, 9 Jan 2024 23:51:52 -0300 Subject: [PATCH] change to mardown --- README.md | 222 +++++++++++++++++++++--------------------------------- 1 file changed, 85 insertions(+), 137 deletions(-) diff --git a/README.md b/README.md index 3bd826d..f1916fe 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,15 @@

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+ +John von Neumann.
"The most vitally characteristic fact about Mathematics is, in my opinion, its quite peculiar relationship to the natural sciences, or, more generally, to any science which interprets experience on a higher than purely descriptive level."
-John von Neumann.

An awesome list of academic resources for STEM (Science, Technology, Engineering, Mathematics) organized by subjects.

@@ -20,16 +18,17 @@ ## Contents -- [Artificial intelligence & Data Science](#artificial-intelligence--data-science) - - [Machine Learning & Neural Networks](#machine-learning--neural-networks) +- [Contents](#contents) +- [Artificial intelligence \& Data Science](#artificial-intelligence--data-science) + - [Machine Learning \& Neural Networks](#machine-learning--neural-networks) - [Big Data](#big-data) - [Natural Language Processing](#natural-language-processing) - [Software tools](#software-tools) @@ -38,172 +37,121 @@ - [Optimization Theory](#optimization-theory) - [Numerical Methods](#numerical-methods) - [Signal Processing](#signal-processing) - - [Signals & Systems](#signals--systems) + - [Signals \& Systems](#signals--systems) - [Digital Signal Processing](#digital-signal-processing) - - [Adaptive Filtering & Statistical Signal Processing](#adaptive-filtering--statistical-signal-processing) -- [Probability & Statistics](#probability--statistics) + - [Adaptive Filtering \& Statistical Signal Processing](#adaptive-filtering--statistical-signal-processing) +- [Probability \& Statistics](#probability--statistics)
## Artificial intelligence & Data Science -

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+- [`course`](https://www.coursera.org/specializations/data-science-python) **Applied Data Science with Python Specialization** - Gain new insights into your data . Learn to apply data science methods and techniques, and acquire analysis skills. University of Michigan. Coursera. +- [`course`](https://www.coursera.org/specializations/advanced-data-science-ibm) **Advanced Data Science with IBM Specialization** - Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. Coursera. +- [`material`](https://github.com/microsoft/AI-For-Beginners/tree/main) **AI-For-Beginners** - 12 Weeks, 24 Lessons, AI for All. ### Machine Learning & Neural Networks -

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+- [`course`](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) **Convolutional Neural Networks** - A DeepLearningAI course on Youtube. +- [`book`](https://libgen.rs/book/index.php?md5=6C72973849A5A771F5D66BF9A2694FC6) [`code`](https://github.com/dynamicslab/databook_matlab) **Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control** - By Steven L. Brunton and J. Nathan Kutz. 1th edition. +- [`book`](https://github.com/xinlin192/DeepLearning/blob/master/Neural%20Networks%20and%20Learning%20Machines%20(3rd%20Edition).pdf) [`code`](https://github.com/dynamicslab/databook_matlab) **Neural Networks and Learning Machines** - By Simon Haykin. 3th edition. +- [`software-tool`](https://github.com/alan-turing-institute/MLJ.jl) **MLJ.jl** - A Julia machine learning framework. +- [`software-tool`](https://pytorch.org/) **pytorch** - Tensors and Dynamic neural networks in Python with strong GPU acceleration. +- [`software-tool`](https://github.com/tensorflow/tensorflow) **Tensorflow** - An Python Open Source Machine Learning Framework for Everyone. +- [`software-tool`](https://github.com/wiseodd/generative-models) **generative-models** - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. +- [`video`](https://www.youtube.com/watch?v=w8yWXqWQYmU) **Building a neural network from scratch.** +- [`video`](https://www.youtube.com/watch?v=JB8T_zN7ZC0) **How convolutional neural networks work, in depth.** +- [`video`](https://www.youtube.com/watch?v=uapdILWYTzE) **MIT 6.S191 (2022): Convolutional Neural Networks.** +- [`dataset`](https://archive.ics.uci.edu) **UCI** - UC Irvine Machine Learning Repository. +- [`dataset`](https://archive.ics.uci.edu) **kaggle** - A data science competition platform enables users to find and publish open datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers. ### Big Data -

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+ +- [`software-tool`](https://github.com/iterative/dvc) **DVC - Data Version Control** - ML Experiments Management with Git. ### Natural Language Processing -

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+- [`software-tool`](https://fasttext.cc/) **fastText** - Library for efficient text classification and representation learning. ### Software tools -

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+- [`software-tool`](https://github.com/khuyentran1401/data-science-template) **cookiecutter** - How to Structure a Data Science Project for Maintainability. +- [`software-tool`](https://github.com/gradio-app/gradio) **gradio** - Build and share delightful machine learning apps, all in Python. +- [`video`](https://www.youtube.com/watch?v=mWgRprKIoIs) **Creating Deep Learning Models Using Keras.** - Deep Learning, Simplilearn. ## Linear Algebra -

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+- [`course`](https://github.com/mitmath/1806) [`code`](https://github.com/shahrokh-bahtooei/Linear-Algebra-Gilbert-Strang) **MIT 18.06, Linear Algebra** - by Professor Gilbert Strang. +- [`book`](https://drive.google.com/file/d/1zdIDyV8qDBWNmmlwhBw7EtLu_pyacdOh/view) [`solution`](https://github.com/8128/SharedResources/blob/master/Introduction%20to%20Linear%20Algebra%205th%202016/Introduction%20to%20Linear%20Algebra%2C%205th%20%20(Solutions)%20%E2%80%93%202016.pdf) [`material`](https://math.mit.edu/~gs/linearalgebra/ila5/indexila5.html) [`code`](https://www.mathworks.com/matlabcentral/fileexchange/2166-introduction-to-linear-algebra) [`code`](https://github.com/shahrokh-bahtooei/Linear-Algebra-Gilbert-Strang) **Introduction to Linear Algebra** - Gilbert Strang. 5th edition. +- [`material`](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra) **The Art of Linear Algebra** - Linear Algebra course by Professor Gilbert Strang. ## Communication Systems -

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+- [`material`](https://www.ieee.li/pdf/essay/quadrature_signals.pdf) **Book Quadrature Signals: Complex, But Not Complicated.** +- [`material`](https://www.researchgate.net/publication/3321471_How_I_learned_to_love_the_trellis) **How I learned to love the trellis.** +- [`material`](http://whiteboard.ping.se/SDR/IQ) **I/Q Data for Dummies.** +- [`material`](https://content.u-blox.com/sites/default/files/products/documents/GLONASS-HW-Design_AppNote_%28GPS.G6-CS-10005%29.pdf) **GLONASS & GPS HW design.** +- [`material`](https://s3.amazonaws.com/embeddedrelated/user/6420/lets_assume_system_synchronized_2_94379.pdf) **Let's Assume the System is Synchronized** - By Fred Harris. +- [`code`](https://people.scs.carleton.ca/~barbeau/SDRCRBook/index.shtml) **Software Radio for Experimenters with GNU Radio** - Implemented in Octave and Python by Michel Barbeau. +- [`software-tool`](https://www.gnuradio.org) **GNU Radio** - Free software development toolkit that provides signal processing blocks to implement software-defined radios and signal processing systems. +- [`software-tool`](https://github.com/RfidResearchGroup/proxmark3) **proxmark3** - Swiss-army tool of RFID, allowing for interactions with the vast majority of RFID tags on a global scale. +- [`software-tool`](https://github.com/greatscottgadgets/hackrf) **HackRF** - A low cost, open source Software Defined Radio platform. ## Optimization Theory -

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+- [`course`](https://see.stanford.edu/Course/EE364A) [`material`](https://web.stanford.edu/class/ee364a/) [`material`](https://github.com/cvxgrp/cvxbook_additional_exercises) **EE364A, Convex Optimization I** - Stanford Engineering Everywhere - Stephen Boyd. +- [`course`](https://web.stanford.edu/class/ee364b/) **EE364b - Convex Optimization II** - Stanford Engineering Everywhere - Stephen Boyd. +- [`course`](https://www.edx.org/learn/engineering/stanford-university-convex-optimization) [`code`](https://github.com/NoamGit/CVX101-HW-with-python) [`code`](https://github.com/PKUFlyingPig/Standford_CVX101) **CVX101 Stanford** - StanfordOnline: Convex Optimization. +- [`book`](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf) [`solution`](https://egrcc.github.io/docs/math/cvxbook-solutions.pdf) [`material`](https://web.mit.edu/~jadbabai/www/EE605/additional_exercises.pdf) **Convex Optimization** - Boyd, S.P. and Vandenberghe, L., 2004. Cambridge university press. +- [`software-tool`](https://www.cvxpy.org/) [`code`](https://github.com/cvxgrp/cvx_short_course/tree/master) **CVXPY** - Open source Python-embedded modeling language for convex optimization problems. +- [`software-tool`](https://github.com/cvxgrp/ncvx) **NCVX** - Python package for modeling and solving problems with convex objectives and decision variables from a nonconvex set. Built on top of CVXPY. +- [`sofware-tool`](http://cvxr.com/) [`material`](http://cvxr.com/cvx/doc/index.html) **CVX** - A Matlab-based convex modeling framework. +- [`sofware-tool`](https://cran.r-project.org/web/packages/CVXR/vignettes/cvxr_intro.html) **CVXR** - R package that provides an object-oriented modeling language for convex optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. +- [`sofware-tool`](https://github.com/jump-dev/Convex.jl) **Convex.jl** - A Julia package for disciplined convex programming. +- [`sofware-tool`](https://github.com/jump-dev/JuMP.jl) **JuMP.jl** - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear). +- [`material`](https://neos-guide.org/guide/types/) **Optimization Problem Types**. +- [`material`](https://dcp.stanford.edu/analyzer) **DCP analyzer**. ## Numerical Methods -

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+- [`book`](https://gdcboysang.ac.in/About/Droid/uploads/Numerical%20Methods.pdf) [`code`](https://github.com/danimateos/numerical_methods) [`code`](https://github.com/asukumari/Numerical-Methods) **Numerical Methods for Engineers** - By Steven C. Chapra and Raymond P. Canale. 7th edition. +- [`software-tool`](https://docs.sciml.ai/DiffEqDocs/latest/) **DifferentialEquations.jl** - A suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. +- [`software-tool`](https://iterativesolvers.julialinearalgebra.org/dev/) **IterativeSolvers.jl** - A Julia package that provides efficient iterative algorithms for solving large linear systems, eigenproblems, and singular value problems. +- [`software-tool`](https://docs.sciml.ai/Integrals/stable/) **Integrals.jl** - An unified interface for the numerical approximation of integrals (quadrature) in Julia. +- [`software-tool`](https://github.com/JuliaDiff/FiniteDifferences.jl) **FiniteDifferences.jl** - A Julia package for numerical differentiation. +- [`software-tool`](https://github.com/JuliaDiff/FiniteDiff.jl) **FiniteDiff.jl** - A Julia package for numerical differentiation. +- [`software-tool`](https://juliadiff.org/ForwardDiff.jl/stable/) **ForwardDiff.jl** - A Julia package for automatic differentiation (AD). +- [`software-tool`](https://github.com/JuliaDiff/ReverseDiff.jl) **ReverseDiff.jl** - A Julia package for automatic differentiation (AD). +- [`software-tool`](https://github.com/JuliaMath/Roots.jl) **Roots.jl** - Root-finding functions for Julia. +- [`software-tool`](https://github.com/JuliaMath/Interpolations.jl) **Interpolations.jl** - Fast, continuous interpolation of discrete datasets in Julia. +- [`software-tool`](https://github.com/SciML/DataInterpolations.jl) **DataInterpolations.jl** - A Julia library for data interpolation and smoothing functions. ## Signal Processing ### Signals & Systems -

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+- [`course`](https://ocw.mit.edu/courses/res-6-007-signals-and-systems-spring-2011/) **MIT OpenCourseWare in Signals And Systems** - An introduction to analog and digital signal processing. ### Digital Signal Processing -

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+- [`course`](https://ocw.mit.edu/courses/6-341-discrete-time-signal-processing-fall-2005/) **MIT OpenCourseWare in Discrete-Time Signal Processing** - It addresses the representation, analysis, and design of discrete time signals and systems. +- [`course`](https://github.com/GuitarsAI/ADSP_Tutorials) **Advanced Signal Processing Notebooks and Tutorials** - By Prof. Dr. -Ing. Gerald Schuller, Applied Media Systems Group, Technische Universität Ilmenau. +- [`book`](https://azrael.digipen.edu/MAT321/DiscreteTimeSignalProcessing3.pdf) [`solution`](https://github.com/cdjhz/Discrete-time-Signal-Processing-Solution/tree/master) **Discrete-Time Signal Processing** - By Alan V. Oppenheim and Ronald W. Schafer. 3th edition. Prentice Hall Signal Processing. +- [`software-tool`](https://github.com/JuliaDSP/DSP.jl) **DSP.jl** - Julia package for filter design, periodograms, window functions, and other digital signal processing functionality. ### Adaptive Filtering & Statistical Signal Processing -

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+- [`course`](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/) [`code`](https://github.com/robical/StatisticalSignalProcessing) **MIT OpenCourseWare 18.065** - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. +- [`book`](https://picture.iczhiku.com/resource/eetop/WYiRoZIFhjsRrXmN.pdf) [`software-tool`](https://www.mathworks.com/matlabcentral/fileexchange/3582-pydaptivefiltering) **Adaptive Filtering Algorithms and Practical Implementation** - By Paulo S. R. Diniz. +- [`book`](https://users.ics.forth.gr/~tsakalid/UVEG09/Book/Haykin-AFT(3rd.Ed.)_Introduction.pdf) [`code`](https://media.pearsoncmg.com/bc/abp/engineering-resources/products/product.html#product,isbn=013267145X) **Adaptive Filter Theory** - By Simon Haykin. 3th edition. +- [`software-tool`](https://matousc89.github.io/padasip/) **padasip** - Python Adaptive Signal Processing Package. +- [`code`](https://github.com/ewan-xu/pyaec/tree/main) **pyaec** - A simple and efficient python implemention of a series of adaptive filters for acoustic echo cancellation. +- [`code`](https://github.com/ninja3697/Kernel-Adaptive-Filtering-in-Python/tree/master) **Kernel Adaptive Filtering in Python** - Implementation of LMS, RLS, KLMS and KRLS filters in Python. +- [`code`](https://github.com/guedes-joaofelipe/adaptive-filtering) **Adaptive Filtering code of Matlab Adaptive Filtering toolbox** - Repository containing a Python implemetation of the Matlab [Adaptive Filtering toolbox. +- [`code`](http://www.mathworks.com/matlabcentral/fileexchange/3582-adaptive-filtering) **Matlab codes for Statistical Signal Processing algorithms** - Matlab code implementing different methods used in statistical signal processing; mainly Extended Kalman Filters, LMS/RLS, Wiener, robust regression, MMSE estimators, ML estimators, Hi-Frequency estimators (Pisarenko, MUSIC, ESPRIT). +- [`code`](https://github.com/YangangCao/AdaptiveFilter) **Code solution of three classical adaptive filter books** - Adaptive Filter Theory (5th Edition) wrotten by Simon Haykin, Adatpive Filtering: Algorithms and Practical Implentation (4th Edition) wrotten by Paulo S R. Diniz, and Adaptive Filters: Theory and Application (2nd Edition) wrotten by Behrouz Farhang-Boroujeny. +- [`code`](https://github.com/rohitner/adaptive-filters) **Collection of implementations of adaptive filters** - Recursive Least Squares, Partial Least Squares, Moving Window Least Squares, Recursive Locally Weighted Partial Least Squares, Online Passive Aggressive Algorithm, Kalman Filter. ## Probability & Statistics -

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+- [`software-tool`](https://github.com/arviz-devs/arviz) **arviz** - Exploratory analysis of Bayesian models with Python. +- [`software-tool`](https://github.com/arviz-devs/ArviZ.jl) **ArviZ.jl** - Exploratory analysis of Bayesian models with Julia. +- [`software-tool`](https://turinglang.org/stable/) **Turing.jl** - Bayesian inference with probabilistic programming.