GRCon19 - gr-satellites: a collection of decoders for Amateur satellites by Daniel Estévez
gr-satellites: a collection of decoders for Amateur satellites by Daniel Estévez gr-satellites is an OOT module encompassing a collection of telemetry decoders that supports nearly 40 different Amateur satellites. This open-source project started in 2015 with the goal of providing telemet
From playlist GRCon 2019
Getting to know grep and the -o option
Normally grep will show the entire matched line. Well maybe you only want the matched data, say for extracting IP Addresses from log files. This is where the option -o is useful. If you are serious about learning grep take a look at my guide : http://bit.ly/2zB2Fu8 Additionally you can
From playlist Getting to grips with grep and perfect your command line searches in Linux and OS X
GRCon19 - The GR PDU Utilities by Jacob Gilbert
The GR PDU Utilities by Jacob Gilbert The GNU Radio PDU Utilities [1] serve to extend functionality of GR’s in-tree PDU API, and enhance user ability to understand and interact with the RF Spectrum. At a basic level there are a number of general purpose PDU ‘feature’ blocks analogous to G
From playlist GRCon 2019
Make Sure to Do This If You Plan to Take the GRE Mathematics Subject Test
In this video I talk about my experience with the GRE Mathematics Subject Test and give some tips for doing well on the test. This is a test that most schools require if you plan to go to graduate school for math. If you are taking the test, then good luck and do your best:) If you have a
From playlist Inspiration and Advice
GRCon19 - A decade of gr-specest -- Free Spectral Estimation! by Martin Braun
A decade of gr-specest -- Free Spectral Estimation! by Martin Braun 10 years ago, the Communications Engineering Lab (CEL) of KIT, Germany, published an out-of-tree module for GNU Radio: The spectral estimation toolbox (gr-specest). Today, it’s still around and works even with the latest
From playlist GRCon 2019
GRCon19 - Exponent: Arbitrary Bandwidth Receiver Architecture by Dana Sorensen
Exponent: Arbitrary Bandwidth Receiver Architecture by Dana Sorensen, Jake Gunther, Colton Lindstrom This paper presents an architecture for receiving arbitrarily wide bandwidth signals using multiple narrowband receivers. Information contained in overlapping spectral regions provides the
From playlist GRCon 2019
PyTorch Autograd Explained - In-depth Tutorial
In this PyTorch tutorial, I explain how the PyTorch autograd system works by going through some examples and visualize the graphs with diagrams. As you perform operations on PyTorch tensors that have requires_grad=True, you build up an autograd backward graph. Then when you call the backwa
From playlist Machine Learning
PyTorch Hooks Explained - In-depth Tutorial
UPDATE: `register_backward_hook()` has been deprecated in favor of `register_full_backward_hook()`. You can read more about `register_full_backward_hook()` here: https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_full_backward_hook In this video, I exp
From playlist Machine Learning
Oxford Calculus: Gradient (Grad) and Divergence (Div) Explained
University of Oxford Mathematician Dr Tom Crawford explains the gradient vector (Grad) and the divergence (Div) for scalar and vector functions. Test yourself with this accompanying FREE worksheet in Maple Learn: https://learn.maplesoft.com/doc/ipubec5cip/trm-grad-and-div-worksheet Sign-
From playlist Oxford Calculus
Div, Grad, and Curl: Vector Calculus Building Blocks for PDEs [Divergence, Gradient, and Curl]
This video introduces the vector calculus building blocks of Div, Grad, and Curl, based on the nabla or del operator. These operators encode physically intuitive notions of rate of change, local divergence, and local rotation. @eigensteve on Twitter eigensteve.com databookuw.com %%% C
From playlist Engineering Math: Vector Calculus and Partial Differential Equations
The spelled-out intro to neural networks and backpropagation: building micrograd
This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school. Links: - micrograd on github: https://github.com/karpathy/micrograd - jupyter notebook
From playlist Neural Networks: Zero to Hero
The Gradient Operator in Vector Calculus: Directions of Fastest Change & the Directional Derivative
This video introduces the gradient operator from vector calculus, which takes a scalar field (like the temperature distribution in a room) and returns a vector field with the direction of fastest change in the temperature at every point. The gradient is a fundamental building block in vec
From playlist Engineering Math: Vector Calculus and Partial Differential Equations
Rod Gover - An introduction to conformal geometry and tractor calculus (Part 4)
After recalling some features (and the value of) the invariant « Ricci calculus » of pseudo‐Riemannian geometry, we look at conformal rescaling from an elementary perspective. The idea of conformal covariance is visited and some covariant/invariant equations from physics are recovered in
From playlist Ecole d'été 2014 - Analyse asymptotique en relativité générale
ME564 Lecture 22: Div, Grad, and Curl
ME564 Lecture 22 Engineering Mathematics at the University of Washington Div, Grad, and Curl Notes: http://faculty.washington.edu/sbrunton/me564/pdf/L22.pdf Course Website: http://faculty.washington.edu/sbrunton/me564/ http://faculty.washington.edu/sbrunton/
From playlist Engineering Mathematics (UW ME564 and ME565)
Reading My Gradschool Rejection Letters
Today I go over some of my failed attempts of getting into phd programs for physics. I applied to 10 schools, most of which rejected me.
From playlist Informative Videos For Physics Majors
System Identification: Koopman with Control
This lecture provides an overview of the use of modern Koopman spectral theory for nonlinear control. In particular, we develop control in a coordinate system defined by eigenfunctions of the Koopman operator. Data-driven discovery of {K}oopman eigenfunctions for control E. Kaiser, J. N
From playlist Data-Driven Control with Machine Learning