companydirectorylist.com  全球商业目录和公司目录
搜索业务,公司,产业 :


国家名单
美国公司目录
加拿大企业名单
澳洲商业目录
法国公司名单
意大利公司名单
西班牙公司目录
瑞士商业列表
奥地利公司目录
比利时商业目录
香港公司列表
中国企业名单
台湾公司列表
阿拉伯联合酋长国公司目录


行业目录
美国产业目录












USA-AL-ENTERPRISE 公司名录

企业名单和公司名单:
CAROLYN L NICHOLSON - PRUDENTIAL TOWN & COUNTRY
公司地址:  1405 Rucker Boulevard,ENTERPRISE,AL,USA
邮政编码:  36330
电话号码:  3343479519 (+1-334-347-9519)
传真号码:  3343932078^^3343933954 (+1-334-393-2078^^3343933954)
网址:  alabamatownandcountry. com
电子邮件:  
美国SIC代码:  6531
美国的SIC目录:  Real Estate

C21/REGENCYREALTYENTERPRISE
公司地址:  531ollWvilCircl,ENTERPRISE,AL,USA
邮政编码:  36331
电话号码:  3345781753 (+1-334-578-1753)
传真号码:  
网址:  infinite-edesigns. com
电子邮件:  
美国SIC代码:  653118
美国的SIC目录:  Real Estate

C21/REGENCY REALTY ENTERPRISE
公司地址:  531 Boll Weevil Circle,ENTERPRISE,AL,USA
邮政编码:  36331
电话号码:  3343470048 (+1-334-347-0048)
传真号码:  
网址:  
电子邮件:  
美国SIC代码:  6531
美国的SIC目录:  Real Estate

C & C CONSTRUCTION
公司地址:  P.O. Box 310608,ENTERPRISE,AL,USA
邮政编码:  36331
电话号码:  3343471918 (+1-334-347-1918)
传真号码:  
网址:  
电子邮件:  
美国SIC代码:  152205
美国的SIC目录:  Builders Service

BUILDERS SHOWCASE
公司地址:  1032 Boll Weevil Cir,ENTERPRISE,AL,USA
邮政编码:  36330-1381
电话号码:  3343938376 (+1-334-393-8376)
传真号码:  3343937887 (+1-334-393-7887)
网址:  
电子邮件:  
美国SIC代码:  571305
美国的SIC目录:  Carpet & Rug Dealers-New

BUDGET TRUCK RENTAL
公司地址:  641 Boll Weevil Cir,ENTERPRISE,AL,USA
邮政编码:  36330-2733
电话号码:  
传真号码:  3343933526 (+1-334-393-3526)
网址:  
电子邮件:  
美国SIC代码:  751303
美国的SIC目录:  Truck Renting & Leasing

BROWN CHARLES E - CHARLES E BROWN INSURANCE
公司地址:  502 E Park Avenue,ENTERPRISE,AL,USA
邮政编码:  36330
电话号码:  3347749614 (+1-334-774-9614)
传真号码:  
网址:  
电子邮件:  
美国SIC代码:  641112
美国的SIC目录:  Insurance

BRENNEMAN DALE D INSURANCE
公司地址:  1303 Rucker Boulevard,ENTERPRISE,AL,USA
邮政编码:  36330
电话号码:  
传真号码:  
网址:  sinironline. org
电子邮件:  
美国SIC代码:  641112
美国的SIC目录:  Insurance

BOYDS MARINE
公司地址:  1041 Geneva Hwy,ENTERPRISE,AL,USA
邮政编码:  36330-3171
电话号码:  3347122897 (+1-334-712-2897)
传真号码:  3343470241 (+1-334-347-0241)
网址:  www. boydsmarine. com
电子邮件:  
美国SIC代码:  555104
美国的SIC目录:  Boat Dealers Sales & Service

BONDYS TOYOTA
公司地址:  519 Boll Weevil Cir,ENTERPRISE,AL,USA
邮政编码:  36330-4065
电话号码:  3343475441 (+1-334-347-5441)
传真号码:  3343475200 (+1-334-347-5200)
网址:  www. bondystoyota. com
电子邮件:  
美国SIC代码:  551102
美国的SIC目录:  Automobile Dealers-New Cars

BIG LOTS
公司地址:  600 Boll Weevil Cir,ENTERPRISE,AL,USA
邮政编码:  36330-2715
电话号码:  2514500205 (+1-251-450-0205)
传真号码:  3343082727 (+1-334-308-2727)
网址:  www. biglots. com
电子邮件:  
美国SIC代码:  531104
美国的SIC目录:  Retail Shops

BERNIEDPARTRIDGEIII-REALTYEXECUTIVES/MEADE&ASSOCIATION
公司地址:  1253RuckroulvrdD,ENTERPRISE,AL,USA
邮政编码:  36330
电话号码:  3347744070 (+1-334-774-4070)
传真号码:  
网址:  andovergreen. com
电子邮件:  
美国SIC代码:  653118
美国的SIC目录:  Real Estate

BERNIEDPARTRIDGEIII-REALTYEXECUTIVES/MEADE&ASSOCIA
公司地址:  1253RuckroulvrdD,ENTERPRISE,AL,USA
邮政编码:  36330
电话号码:  3347744070 (+1-334-774-4070)
传真号码:  
网址:  andovergreen. com
电子邮件:  
美国SIC代码:  653118
美国的SIC目录:  Real Estate

Show 144-156 record,Total 165 record
First Pre [7 8 9 10 11 12 13] Next Last  Goto,Total 13 Page










公司新闻:
  • What are the features get from a feature extraction using a CNN?
    So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood how a CNN works
  • machine learning - What is a fully convolution network? - Artificial . . .
    Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
  • What is the fundamental difference between CNN and RNN?
    CNN vs RNN A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis In a very general way, a CNN will learn to recognize components of an image (e g , lines, curves, etc ) and then learn to combine these components
  • Extract features with CNN and pass as sequence to RNN
    But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images
  • What is a cascaded convolutional neural network?
    The paper you are citing is the paper that introduced the cascaded convolution neural network In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN) This combination requires the introduction of a new input feature which fulfills the "cascade manner" and
  • Reduce receptive field size of CNN while keeping its capacity?
    One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv and now followed by 4 times a 1x1 conv layer instead of the original 3x3 convs (which increase the receptive field)) In doing that, the number of parameters can be kept at a similar level While 1x1 convolutions are
  • In a CNN, does each new filter have different weights for each input . . .
    Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel So the diagrams showing one set of weights per input channel for each filter are correct
  • How to handle rectangular images in convolutional neural networks . . .
    I think the squared image is more a choice for simplicity There are two types of convolutional neural networks Traditional CNNs: CNNs that have fully connected layers at the end, and fully convolutional networks (FCNs): they are only made of convolutional layers (and subsampling and upsampling layers), so they do not contain fully connected layers With traditional CNNs, the inputs always need
  • convolutional neural networks - When to use Multi-class CNN vs. one . . .
    I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN




企业名录,公司名录
企业名录,公司名录 copyright ©2005-2012 
disclaimer |iPhone手机游戏讨论 |Android手机游戏讨论 |海外商家点评 |好笑有趣影片图片