neodelphis

def conv_backward_naive(dout, cache):
    """
    A naive implementation of the backward pass for a convolutional layer.

    Inputs:
    - dout: Upstream derivatives.
    - cache: A tuple of (x, w, b, conv_param) as in conv_forward_naive

    Returns a tuple of:
    - dx: Gradient with respect to x

cb372

(This is a translation of the original article in Japanese by moratorium08.)

(UPDATE (22/3/2019): Added some corrections provided by the original author.)

Writing your own OS to run on a handmade CPU is a pretty ambitious project, but I've managed to get it working pretty well so I'm going to write some notes about how I did it.

odashi

#include <iomanip>
#include <iostream>
#include <cstdlib>
#include <vector>

#include <cuda.h>
#include <cudnn.h>

#define CUDA_CALL(f) { \
  cudaError_t err = (f); \

mayankgrwl97

def conv_backward(dH, cache):
    '''
    The backward computation for a convolution function
    
    Arguments:
    dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1
    cache -- cache of values needed for the conv_backward(), output of conv_forward()
    
    Returns:
    dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1

mishal23

/**
 * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
 *
 * Please refer to the NVIDIA end user license agreement (EULA) associated
 * with this source code for terms and conditions that govern your use of
 * this software. Any use, reproduction, disclosure, or distribution of
 * this software and related documentation outside the terms of the EULA
 * is strictly prohibited.
 *
 */

roadrunner2

Introduction

This is about documenting getting Linux running on the late 2016 and mid 2017 MPB's; the focus is mostly on the MacBookPro13,3 and MacBookPro14,3 (15inch models), but I try to make it relevant and provide information for MacBookPro13,1, MacBookPro13,2, MacBookPro14,1, and MacBookPro14,2 (13inch models) too. I'm currently using Fedora 27, but most the things should be valid for other recent distros even if the details differ. The kernel version is 4.14.x (after latest update).

The state of linux on the MBP (with particular focus on MacBookPro13,2) is also being tracked on https://github.com/Dunedan/mbp-2016-linux . And for Ubuntu users there are a couple tutorials (here and here) focused on that distro and the MacBook.

Note: For those who have followed these instructions ealier, and in particular for those who have had problems with the custom DSDT, modifying the DSDT is not necessary anymore - se

sfujiwara

# -*- coding: utf-8 -*-

import numpy as np
from scipy.sparse.linalg import cg
import tensorflow as tf
import time


def conjugate_grad(A, b, x=None):
    """

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

creationix

// Load the TCP Library
net = require('net');

// Keep track of the chat clients
var clients = [];

// Start a TCP Server
net.createServer(function (socket) {

  // Identify this client