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#!/bin/bash

# Download GitKraken
wget https://release.gitkraken.com/linux/gitkraken-amd64.tar.gz

# copy the downloaded file into /opt directory
cp gitkraken-amd64.tar.gz /opt/gitkraken

cd /opt

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Convolutional Neural Network For Sentence Classification

Introduction

• The paper demonstrates how simple CNNs, built on top of word embeddings, can be used for sentence classification tasks.
• Link to the paper
• Implementation

Architecture

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Intel for display, NVIDIA for computing

This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 desktop.

I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti. Both of them can be shown via lspci | grep VGA.

00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)

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# Written by Luis Mesas
import threading
import time
import cv2

# Define video capture class
class VideoCaptureAsync:
    def __init__(self, src=0, width=640, height=480, driver=None):
        self.src = src
        if driver is None:

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import cv2
# Import the video capturing function
from video_capture import VideoCaptureAsync
import time

#Specify width and height of video to be recorded
vid_w = 1280
vid_h = 720
#Intiate Video Capture object
capture = VideoCaptureAsync(src=0, width=vid_w, height=vid_h)

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# Import the recording process
from camera_record import record_video
# set recording duration
record_time = 20
# To run this as part of a bigger program, use mutltiprocessing to create a seperate process 
# for video recording using the following lines.
from multiprocessing import Process
# The line above can go to the top of your file and the following lines should be placed 
# where ever you need to record a video
# Create the process thread object

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from numpy import linalg as la

def nearestPD(A):
    """Find the nearest positive-definite matrix to input

    A Python/Numpy port of John D'Errico's `nearestSPD` MATLAB code [1], which
    credits [2].

    [1] https://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd

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class AE(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        self.encoder_hidden_layer = nn.Linear(
            in_features=kwargs["input_shape"], out_features=128
        )
        self.encoder_output_layer = nn.Linear(
            in_features=128, out_features=128
        )
        self.decoder_hidden_layer = nn.Linear(

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