wolfv

name: CI

on:
  push:
    branches:
    - main
  pull_request:
    branches:
    - main

madelinegannon

Azure Kinect on Ubuntu 18.04

Notes on Installing Microsoft Azure Kinect Sensor and Body Tracking SDKs on Linux PC and NVIDIA Jetson Xavier NX

06.12.2020

Jump to:

• Installing Sensor SDK on Linux PC
• Installing on Jetson Xavier NX
• Installing Body Tracking SDK
• Updating Firmware for Azure Kinect

sbarratt

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

gocarlos

// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle

#include <Eigen/Dense>

Matrix<double, 3, 3> A;               // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B;         // Fixed rows, dynamic cols.
Matrix<double, Dynamic, Dynamic> C;   // Full dynamic. Same as MatrixXd.
Matrix<double, 3, 3, RowMajor> E;     // Row major; default is column-major.
Matrix3f P, Q, R;                     // 3x3 float matrix.

samrocketman

Why this document?

I upgraded my iPhone 5s to iOS 10 and could no longer retrieve photos from it. This was unacceptable for me so I worked at achieving retrieving my photos. This document is my story (on Ubuntu 16.04).

The solution is to compile libimobiledevice and ifuse from source.

Audience

Who is this guide intended for?

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

cbsmith

// -*- compile-command: "clang++ -ggdb -o random_selection -std=c++0x -stdlib=libc++ random_selection.cpp" -*-
//Reference implementation for doing random number selection from a container.
//Kept for posterity and because I made a surprising number of subtle mistakes on my first attempt.
#include <random>
#include <iterator>

template <typename RandomGenerator = std::default_random_engine>
struct random_selector
{
	//On most platforms, you probably want to use std::random_device("/dev/urandom")()