Machine Learning & Neural Networks

• `code` - models** - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
• `code` - from-scratch** - Implementing a ChatGPT-like LLM from scratch, step by step.
• `course` - A DeepLearningAI course on YouTube.
• `course` - f22/) [`code`](https://github.com/bwilder0/10606-f23/tree/main) **Mathematical Foundations for Machine Learning** - 10-606, Fall 2022. Carnegie Mellon University (CMU).
• `book` - resources/products/product.html#product,isbn=0131471392) [`code`](https://github.com/irustandi/Neural-Networks-and-Learning-Machines-Haykin) [`code`](https://www.mathworks.com/matlabcentral/fileexchange/123880-fundamentals-of-neural-networks) [`code`](https://github.com/ocimakamboj/NNLS?tab=readme-ov-file) **Neural Networks and Learning Machines** - By Simon Haykin. 3th edition.
• `course` - A DeepLearningAI course on YouTube.
• `course` - 10-315, Spring 2023. Carnegie Mellon University (CMU).
• `course` - f22/) [`code`](https://github.com/bwilder0/10606-f23/tree/main) **Mathematical Foundations for Machine Learning** - 10-606, Fall 2022. Carnegie Mellon University (CMU).
• `book` - Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control** - By Steven L. Brunton and J. Nathan Kutz. 1th edition.
• `book` - book.github.io/) [`code`](https://github.com/mml-book/mml-book.github.io) **Mathematics for Machine Learning Book** - by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth. <!-- It uses numerator layout! -->
• `video` - Deep Learning, Simplilearn.
• `video`
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• `video` - off.**

• `course` - Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills. University of Michigan. Coursera.
• `course` - Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. Coursera.
• `reading` - For-Beginners** - A 12 Weeks, 24 Lessons, AI for All.

Machine Learning & Neural Networks

• `course` - bahtooei/Linear-Algebra-Gilbert-Strang) **MIT 18.06, Linear Algebra** - By Professor Gilbert Strang.
• `course` - **Abstract Algebra** - A YouTube course from Socratica.
• `reading` - Linear Algebra course by Professor Gilbert Strang.
• `video` - **What is Jacobian?** Multivariable calculus: The right way of thinking derivatives and integrals.
• `book` - introduction-to-linear-algebra) **Introduction to Linear Algebra** - Gilbert Strang. 5th edition.
• `video` - **What is Jacobian?** Multivariable calculus: The right way of thinking derivatives and integrals.
• `course` - **Abstract Algebra** - A YouTube course from Socratica.

GNSS

• `video` - **GPS Spoofing With The HackRF On Windows**
• `reading` - **GNSS data processing with Python** - Hands-on tutorials for GNSS data processing using Python and Jupyter Notebooks/book.
• `reading`
• `code` - GPS Toolbox topical collection of the journal GPS Solutions. It provides a means for distributing the source code and algorithms discussed in the GPS Toolbox topical collection.
• `video` - **GPS Spoofing With The HackRF On Windows**

Machine Learning & Neural Networks

• `reading`
• `reading` - By Fred Harris.
• `reading`
• `reading`
• `code` - Implemented in Octave and Python by Michel Barbeau.
• `course` - seminars/design-handbooks/Software-Defined-Radio-for-Engineers-2018/SDR4Engineers.pdf) [`reading`](https://wpi.cleancatalog.net/electrical-and-computer-engineering/ece-4305) [`reading`](https://sdrforengineers.github.io/) [`reading`](https://ieeexplore.ieee.org/document/6815911) [`reading`](https://github.com/tapyu/ece4305-sdr-systems-and-analysis) [`reading`](https://www.analog.com/media/en/news-marketing-collateral/product-highlight/ADALM-PLUTO-Product-Highlight.pdf) [`reading`](https://wiki.analog.com/university/tools/pluto) [`reading`](https://courses.washington.edu/ee506/projects/asee2011.pdf) [`reading`](https://newsdr.org/workshops/sdr-classroom-panel/) [`reading`](https://github.com/sdrforengineers/LectureMaterials) [`reading`](https://www.mathworks.com/hardware-support/adalm-pluto-radio.html) [`reading`](https://github.com/sdrforengineers/LabGuides) [`reading`](https://www.analog.com/en/resources/evaluation-hardware-and-software/evaluation-boards-kits/adalm-pluto.html#eb-overview) [`video`](https://www.analog.com/en/resources/media-center/videos/5765544380001.html) [`video`](https://www.analog.com/en/resources/media-center/videos/5764861961001.html) [`video`](https://www.youtube.com/watch?v=9CMX3b-4RUw&ab_channel=WPIECE) [`video`](https://www.analog.com/en/resources/media-center/videos/5839800192001.html) [`code`](https://github.com/sdrforengineers/code) - **ECE4305 Software Defined Radio Systems and Analysis** - Hands-on course on SDR communication system from the Department of Electrical and Computer Engineering (ECE), at Worcester Polytechnic Institute (WPI), taught by Prof. Dr. Alexander Wyglinski. The course is based on the book "Software-Defined Radio for Engineers", developed by Analog Devices Inc. (ADI) and written by Travis F. Collins et. al., and uses ADALM-PLUTO SDR, also manufactured by ADI.
• `course` - support/rtl-sdr.html) [`reading`](https://www.rtl-sdr.com/about-rtl-sdr/) [`code`](https://github.com/tapyu/stewart-rtl-sdr-using-matlab) **Software-Defined Radio Using MATLAB, Simulink, and the RTL-SDR** - A hands-on course on SDR using the Realtek RTL2832U chip. It is based on the book "Software Defined Radio Using MATLAB & Simulink and the RTL-SDR", by Bob Stewart. It uses the RTL-SDR device, emerged from the repurposing of digital TV (DVB-T) USB dongles that use the RTL2832U chipset, which was originally developed by Realtek Semiconductor Corp.
• `course` - Video series of a complete course in Software Defined Radio (SDR). This course builds flexible SDR applications using GNU Radio through exercises that will help you learn the fundamentals of Digital Signal Processing (DSP) needed to master SDR. For the over-the-air exercises, you’ll need a HackRF One or other SDR peripheral.
• `course` - A single and long webinar provided by Analog Devices Inc. (ADI). It covers many technical and theoretical aspects of radiofrequency front-end (RF-FE) signal chains. It also mentions ADALM-PLUTO SDR, a software-defined radio deviced manufactured by this company and extensively used for learning purposes by hobbists and students.

GNSS

• `course` - By Steve Brunton. Introduction and overview to Differential Equations & Dynamical Systems. Dynamical systems are differential equations that describe any system that changes in time
• `book` - Methods) **Numerical Methods for Engineers** - By Steven C. Chapra and Raymond P. Canale. 7th edition.

Optimization Theory

• `video` - An intuitive explanation of convex optimization and how it works, from the YouTube channel Visually Explained.
• `course` - Stanford Engineering Everywhere - Stephen Boyd.
• `course` - Convex Optimization II** - Stanford Engineering Everywhere - Stephen Boyd.
• `course` - HW-with-python) [`code`](https://github.com/PKUFlyingPig/Standford_CVX101) **CVX101 Stanford** - StanfordOnline: Convex Optimization.
• `book` - solutions.pdf) [`reading`](https://web.mit.edu/~jadbabai/www/EE605/additional_exercises.pdf) **Convex Optimization** - Boyd, S.P. and Vandenberghe, L., 2004. Cambridge university press.
• `reading`
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Digital Signal Processing

• `course` - By Prof. Dr. -Ing. Gerald Schuller, Applied Media Systems Group, Technische Universität Ilmenau.
• `course` - Time Signal Processing** - It addresses the representation, analysis, and design of discrete time signals and systems.
• `book` - time-Signal-Processing-Solution/tree/master) **Discrete-Time Signal Processing** - By Alan V. Oppenheim and Ronald W. Schafer. 3th edition. Prentice Hall Signal Processing.

Adaptive Filtering & Statistical Signal Processing

• `book` - and-Bayesian-Filters-in-Python) **Kalman and Bayesian Filters in Python** - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more.
• `code` - Repository containing a Python implemetation of the Matlab Adaptive Filtering toolbox.
• `code` - 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` - Recursive Least Squares, Partial Least Squares, Moving Window Least Squares, Recursive Locally Weighted Partial Least Squares, Online Passive Aggressive Algorithm, Kalman Filter.
• `course` - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.
• `book` - pydaptivefiltering) **Adaptive Filtering Algorithms and Practical Implementation** - By Paulo S. R. Diniz.
• `book` - resources/products/product.html#product,isbn=013267145X) **Adaptive Filter Theory** - By Simon Haykin. 3th edition.
• `code` - A simple and efficient python implemention of a series of adaptive filters for acoustic echo cancellation.
• `code` - Implementation of LMS, RLS, KLMS and KRLS filters in Python.
• `code` - 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).
• `course` - filter-design-examples.html?s_eid=PSM_15028) [`reading`](https://www.mathworks.com/help/control/ug/kalman-filtering.html) [`reading`](https://www.mathworks.com/discovery/kalman-filter.html?s_eid=PSM_15028) [`code`](https://www.mathworks.com/matlabcentral/fileexchange/105525-kalman-filter-virtual-lab) - **Mathworks course on Kalman filtering**.

Signals & Systems

• `course` - An introduction to analog and digital signal processing.

System identification

• `course` - A course on System Identification from the University of Colorado, taught by Prof. Dr. Gregory L. Plett.
• `course` - to-system-identification-81796.html) **MathWorks course on System Identification** - Taught by Lennart Ljung, Linköping University.