From 1c252d21bfb2294e8f024c9288aebfaf24dcfdf7 Mon Sep 17 00:00:00 2001 From: Rubem Pacelli Date: Thu, 18 Jan 2024 19:11:04 -0300 Subject: [PATCH] Update README.md --- README.md | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index b97e61e..0edae21 100644 --- a/README.md +++ b/README.md @@ -47,8 +47,8 @@ ## Algorithm Theory -- [`video`](https://www.youtube.com/watch?v=A60q6dcoCjw) **The hidden beauty of the A\* algorithm** - A reading material. -- [`video`](https://www.youtube.com/watch?v=EFg3u_E6eHU) **How Dijkstra's Algorithm Works** - A reading material. +- [`video`](https://www.youtube.com/watch?v=A60q6dcoCjw) **The hidden beauty of the A\* algorithm.** +- [`video`](https://www.youtube.com/watch?v=EFg3u_E6eHU) **How Dijkstra's Algorithm Works.** ## Artificial Intelligence & Data Science - [`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. @@ -70,9 +70,10 @@ - [`software`](https://github.com/aeon-toolkit/aeon) **aeon** - A toolkit for conducting machine learning tasks with time series data. - [`software`](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=mWgRprKIoIs) **Creating Deep Learning Models Using Keras.** - Deep Learning, Simplilearn. -- [`video`](https://www.youtube.com/watch?v=w8yWXqWQYmU) **Building a neural network from scratch** - A reading material. -- [`video`](https://www.youtube.com/watch?v=JB8T_zN7ZC0) **How convolutional neural networks work, in depth** - A reading material. -- [`video`](https://www.youtube.com/watch?v=uapdILWYTzE) **MIT 6.S191 (2022): Convolutional Neural Networks** - A reading material. +- [`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.** +- [`video`](https://www.youtube.com/watch?v=vhz2pgfOpaw) **Bias Variance trade-off/** ### Big Data @@ -93,10 +94,10 @@ - [`hardware`](https://github.com/greatscottgadgets/hackrf) **HackRF** - A low cost, open source Software Defined Radio platform. - [`hardware`](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`](https://www.gnuradio.org) **GNU Radio** - Free software development toolkit that provides signal processing blocks to implement software-defined radios and signal processing systems. -- [`reading`](https://www.ieee.li/pdf/essay/quadrature_signals.pdf) **Book Quadrature Signals: Complex, But Not Complicated** - A reading material. -- [`reading`](https://www.researchgate.net/publication/3321471_How_I_learned_to_love_the_trellis) **How I learned to love the trellis** - A reading material. -- [`reading`](http://whiteboard.ping.se/SDR/IQ) **I/Q Data for Dummies** - A reading material. -- [`reading`](https://content.u-blox.com/sites/default/files/products/documents/GLONASS-HW-Design_AppNote_%28GPS.G6-CS-10005%29.pdf) **GLONASS & GPS HW design** - A reading material. +- [`reading`](https://www.ieee.li/pdf/essay/quadrature_signals.pdf) **Book Quadrature Signals: Complex, But Not Complicated.** +- [`reading`](https://www.researchgate.net/publication/3321471_How_I_learned_to_love_the_trellis) **How I learned to love the trellis.** +- [`reading`](http://whiteboard.ping.se/SDR/IQ) **I/Q Data for Dummies.** +- [`reading`](https://content.u-blox.com/sites/default/files/products/documents/GLONASS-HW-Design_AppNote_%28GPS.G6-CS-10005%29.pdf) **GLONASS & GPS HW design.** - [`reading`](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. @@ -112,8 +113,8 @@ - [`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). -- [`reading`](https://neos-guide.org/guide/types/) **Optimization Problem Types** - A reading material. -- [`reading`](https://dcp.stanford.edu/analyzer) **DCP analyzer** - A reading material. +- [`reading`](https://neos-guide.org/guide/types/) **Optimization Problem Types.** +- [`reading`](https://dcp.stanford.edu/analyzer) **DCP analyzer.** ## Numerical Methods