kuasha

// FROM: https://releases.llvm.org/2.2/docs/tutorial/JITTutorial1.html

#include <llvm/IR/Module.h>
#include <llvm/IR/Function.h>
#include <llvm/IR/CallingConv.h>
#include "llvm/IR/IRBuilder.h"
#include "llvm/IR/BasicBlock.h"


int main(int argc, char **argv)

kuasha

#!/bin/bash
export MAKEFLAGS="-j 3"
set -e

VERSION="1.18.0"
LIBNICE_VERSION="0.1.17" # libnice (v>=0.1.14) needed for webrtcbin
LIBSRTP_VERSION="2.3.0" # libsrtp (v>=2.2.0) required for srtp plugin
WEBRTCAUDIO_VERSION="0.3.1" # webrtc-audio-processing required for webrtcdsp
USRSCTP_VERSION="0.9.3.0" # usrsctp required for webrtc data channels (sctp)

kuasha

//Initialize the Lidar
#include <Wire.h>
#include <LIDARLite.h>

LIDARLite myLidarLite;

//Initialize the Servo
#include <Servo.h>

Servo myservo;  // create servo object to control a servo

kuasha

sudo apt-get install libtool pkg-config build-essential autoconf automake uuid-dev

git clone git://github.com/zeromq/libzmq.git
cd libzmq
./autogen.sh
# do not specify "--with-libsodium" if you prefer to use internal tweetnacl security implementation (recommended for development)
./configure --with-libsodium
make check
sudo make install
sudo ldconfig

kuasha

Installing Nvidia drivers on Ubuntu 16.04 for GTX1070

Build Specification:

• Intel i7-6850K @ 3.60GHz
• GTX 1070

Prerequisites

• Fresh install of Ubuntu 16.04

Note - did not encrypt home directory or xda drive

kuasha

#include <Servo.h>

Servo myservo;
Servo stearing;

int state = 99;
const int trigPin = 2;
const int echoPin = 4;

// for power servo

kuasha

/*
  Analog Input
 Demonstrates analog input by reading an analog sensor on analog pin 0 and
 turning on and off a light emitting diode(LED)  connected to digital pin 13.
 The amount of time the LED will be on and off depends on
 the value obtained by analogRead().

 The circuit:
 * Potentiometer attached to analog input 0
 * center pin of the potentiometer to the analog pin

kuasha

### Preprocess the data here.
### Feel free to use as many code cells as needed.

from tensorflow.contrib.layers import flatten

def LeNet(x):    
    # Hyperparameters
    mu = 0
    sigma = 0.1
    

kuasha

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

kuasha

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))