Create puias-computational.repo file:
$ vim /etc/yum.repos.d/puias-computational.repo
Paste the following code into the above file:
import urllib2 | |
from lxml import html | |
from collections import defaultdict | |
url = "http://www.youtube.com/watch?v=IyLZ6RXJ8Eg" | |
doc = html.parse(urllib2.urlopen(url)) | |
data = defaultdict(dict) | |
props = doc.xpath('//meta[re:test(@name|@property, "^twitter|og:.*$", "i")]', | |
namespaces={"re": "http://exslt.org/regular-expressions"}) |
muduo::AtomicInt32 g_running_clients; | |
EventLoop* g_loop = nullptr; | |
class Client: boost::noncopyable { | |
private: | |
const char* server_; | |
ifstream input_; | |
muduo::AtomicInt32 running_reqs_; | |
curl::Curl curl_; | |
curl::RequestPtr req_user_, req_target_; |
vex::vector<double> h(ctx, n1 * n2); | |
vex::vector<uint32_t> N(ctx, n1 * n3); | |
vex::vector<double> V(ctx, n4 * n2); | |
vex::vector<double> L(ctx, n1 * n3); | |
for (int i = 0; i < h.rows(); ++i) { | |
for (int j = 0; j < N.cols(); ++j) { | |
row(V, N(i,j)) -= L(i, j) * row(h, i); | |
} | |
} |
VEX_FUNCTION(float, l2_distance, (size_t, idx)(uint32_t, num_dim)(float*, query)(float*, candidates), | |
float d = 0; | |
for (uint i = 0; i < num_dim; ++i) { | |
d += pow(query[i] - candidates[idx * num_dim + i], 2); | |
} | |
return d; | |
); | |
Knn::ValueVec Knn::search_h(const ValueVec& query, size_t query_offset) const { | |
prof_.tic_cl("distance"); |
var async = require('async'); | |
var express = require('express'), | |
app = express(); | |
var google = require('googleapis'), | |
OAuth2Client = google.auth.OAuth2; | |
var oauth2Client = new OAuth2Client(CLIENT_ID, CLIENT_SECRET, REDIRECT_URL); |
A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.
Overview
Symbolic: Theano, CGT; Automatic: Torch, MXNet
Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.