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          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          Android Smart Linkify API背后的机器学习      Cache   Translate Page      
2018年8月初,谷歌正式发布了代号为“Pie”的Android 9,Android正在推出一系列由人工智能提供支持的新功能,Android Smart Linkify就是其中最重要的新AI功能之一。 Smart Linkify建立在先前版本Android Oreo发布的Smart Text Selection之上。Smart Linkify可以检测文本中的某些类型的实体(例如地址、电话号码)并添加可点击的链接,允许用户直接启动地图或拨打电话。它由设备内的前馈神经网络提供支持,每种语言大小仅500KB,推理代码不超过250KB。这个系统为几近实时的系统,在Google Pixel手机上计算时间不到20毫秒。 系统首先通过空格将输入文本拆分为单词,并计算最多15个单词所有可能的单词子序列。每个子序列被提供给神经网络,神经网络基于其有效性为它们分配[0 ... 1]范围的值。在删除重叠实体后,系统为子序列打较高的分数。在整个过程的第一部分结束时,每个未知类型都有一个不重复单词子序列。 然后使用第二个神经网络来识别每个单词子序列的类型,无论是电话号码、地址还是未识别的实体。神经网络将上下文中的单词子序列作为输入。通过将子序列的前三个和后三个单词作为实体,将它们前面的五个单词作为左上下文,将随后的五个单词作为右上下文,然后将它们作为不同的特征来识别单词的含义。这个神经网络中一个有趣的优化是使用二进制特征来识别以大写字母开头的单词。其背后的原因是,网络邮政地址非常独特,使用这种方式更容易识别出来。 为了训练神经网络,谷歌团队从真实数据中生成了虚假而实际的样本。他们使用 Schema.org 注解的实体、地址、电话号码和随机单词的自定义列表,合成了一个训练集。他们采用可观察的实体并用随机单词围绕它们来达到更理想的结果。另外,他们还有意生成负数据来训练样本,让神经网络避免将“ID:”等短语识别为电话号码。 国际化是该功能的一个重要方面,根据测试,一种模型适用于所有拉丁语言,并可以为中文、日文、韩文、泰文、阿拉伯文和俄文添加单独的模型。目前,API支持16种语言,未来几个月将支持更多语言。这些模型使用TensorFlow进行训练,自定义的推理库由TensorFlow Lite和 FlatBuffers 提供支持。开发人员可以通过 TextClassifier API 的 generateLinks 方法开始使用Smart Linkify。 *参考来源:infoq,米雪儿编译整理,转载请注明来自 FreeBuf.COM
           华映资本章高男: 当所有人重注AI,我们看好这三大方向       Cache   Translate Page      

过去八年总投资额超过1300亿元人民币、行业企业获得投资几率高于其他行业2-3倍,BAT与VC纷纷押“重注”入局……如果说能确切预见到的未来趋势,人工智能是热点之一。

从IT(Information Technology)转向DT(Data Technology)的当下,任何行业和企业面对人工智能的发展都不敢怠慢,而就在今天宣布“退休”的马云老师,也曾在演讲中公开表示,希望倡导教育改革,帮助年轻人适应人工智能时代的来临。

近日,在2018“创响中国”创新创业大赛复赛颁奖典礼暨“人工智能&大健康”论坛上,华映资本合伙人章高男围绕以上三个问题展开了分享。他认为:AI不是风口,是现实。人工智能可以简化成四个层次:

首先,人工智能需要利用已有的经验, 这个经验的载体是海量数据;其次,有了经验以后,要通过各种数学模型去逼近这些经验,即算法和算力层;通过模型预测未来,将预测应用到不同的领域,就是各种学科;把学科应用到场景,就变成了商业应用。

面对人工智能的发展,章高男看好以下三大投资方向:

 AI+行业:强场景、强刚需,AI赋能升级产业模式。如智能物流,自动驾驶,无人仓储,安防;无人零售(深兰科技)等;

AI paas 平台:能够降低AI使用门槛,提升效率。如天云大数据,第四范式等;

底层技术:AI 芯片,前端传感器等。

renwu-zhanggaonan.webp.jpg

华映资本合伙人章高男

以下为章高男演讲实录(经整理):

大家下午好,很高兴今天能够在这里跟大家做一个关于人工智能的探讨,其实人工智能对很多人来说已经是耳熟能详了,但每个人对人工智能的理解以及层次是不一样的。因为今天在座的很多都不是技术出身,所以我尽可能把人工智能这个技术领域简化成一个模型,方便大家去探讨。

人工智能到底是什么?

我把人工智能简化分成四个层次:

人工智能四层次

首先人工智能要利用已有的经验,而这个经验的载体就是海量数据。大部分的人工智能依靠的机器学习方法,都是有监督的学习,或者半监督的学习。当然也有一些简单的强化学习,但效果相对有限。既然是监督学习,首先要有数据,要利用已有的经验。

第二步有了经验以后,要通过各种数学模型去逼近这些经验,这就是算法和算力层。算法是理论基础,算力是工程实现。算法框架是人工智能的核心,所以国际领先的大公司都在不计成本打造,例如Tensorflow,Torch等。

第三步有了模型,下一步就是通过模型预测未来是怎么样的,这个预测应用到不同的领域,就是各种学科。例如,语音识别,计算机视觉,NLP,推荐,动态规划等。

最后把学科应用到场景,就变成了商业应用。例如自动驾驶,滴滴的路径动态规划,头条的信息流推荐,智能音响等等。今天下棋人类已经下不过机器,不仅是下棋,AI还可以去帮你做诗做曲,还有精准营销,安防等等。

有人说AI是泡沫,有人说AI是风口,如果你真正了解什么是AI,你会发现其实AI不是风口,AI已经在我们的工作生活中有了大量实际的应用,而且是广泛的应用。按上述四个层面分类,如今每个层次都已诞生出百亿千亿级的大公司。比如数据层的云计算,算法算力层的Tensorflow,TPU;学科层的商汤科技;应用层的今日头条等等。

AI不是风口,是现实

我判断AI已经成为现实,有如下理由:

AI风口

第一,在技术层面,AI行业已经高度工程化。首先AI需要海量数据,今天企业拥有TB (Trillionbyte:万亿字节)级的数据已经很容易,大量小微企业都拥有足够多的数据,这在十年或者二十年以前是不可想象的,所以这是一个巨大的改变。今天企业拥有数据已经不是一个很大的门槛,都可以去做AI。

另外算法和算力也实现了大规模工程化。算法算力有国际大的公司的支持,他们已经提供了很好的平台和应用的框架,这都是已经可以工程化的框架。所以我们使用AI的门槛会大大降低。

当然这些算法的能力是有限的,今天我们想指望AI去产生思想和思维还不太现实,但是你只要给它一个明确边界,很多场景AI会比人做的好。譬如下棋是在死活约束和方格约束的前提下,需求最优面积的动态规划问题。有了这个边界,AI可以远超于人。而且最重要的是只要给出边界,它们的算力算法都有很好的工程化的方案,所以今天应用AI已经不是很难的事情。

第二点,除了技术工程化,AI要被广泛应用还要有刚需。事实上目前AI已经是企业里最大的刚需之一了。主要有两个原因:

首先,今天企业拥有的数据的维度,跟过去比,已经大大丰富。过去十年二十年前,企业里的数据很简单,无非是ERP和财务数据,拥有这些数据的企业已经是很好的企业了。但今天不一样,譬如企业要通过线上去卖东西,那么你选择天猫还是京东,产品的排位等等, 都有大量的数据需要商业决定。

当企业的数据维度有几百维的时候,很难用简单的BI(Business Intelligence)用因果关系类来分析了。这个问题要去解决,就必须应用到机器学习的方法了。

另外,今天企业拥有大量非结构化数据。比如说语音、视频、文字,图象,这些非结构化的数据只有机器学习的方法去做,用BI是没法处理这些数据的。比如我们的客服对销售的影响越来越重要,但是客服的通话记录是文字你怎么去优化呢?如果会用NLP和分类的方法,它可以产生更多的销售。

所以从上述两点讲企业对AI有强烈的需求。我们也看到很多传统行业,会有很多优化的需求。

这是我刚才的一个观点,这个观点还需要有一些数据的支持。下图是整个AI在过去几年投资的金额和项目数,黄色的曲线线是投资金额,去年已经投了将近600亿在AI相关的企业。

hangye-AI3.webp.jpg

再看第二个图,是从16年跟17年一对比,就会明显发现,16年投资集中在在早期,B轮、C轮,而17年到高点是C轮、D轮,这说明他们成长的很好,行业向成熟发展。

AI投资

再看一下人工智能公司,是资本的绝对热点。人工智能获投的公司以及未获投的公司,它的比例已经超过50%,也就是意味着每两家人工智能公司,就有一家会融到钱,这个在别的行业几乎无法想象的。无论是国内还是国外,投资机构都在对AI下重注。如果是一到两家这样做,那可能是去赌,但如果所有人去做,这说明了什么?大家可以思考一下。

hangye-AI5.webp.jpg

再从另一个维度,即企业端的态度来看AI。大家印象最深的是百度all in AI,其实何止百度,谷歌,跟微软, IBM,还有国内的阿里,包腾讯都在AI是全产业链布局的。无论从最底层的算力层,还是核心的到基础框架层,再到应用层,今天国内外最值钱的公司,他们都是在AI不计成本去投资布局的。

尤其在算法框架层,因为算法框架层是机器学习的核心,整个AI里面最核心的部分,但是这部分基本上由国外巨头主导,这个差距我们必须得认。当我们发现全球所有最成功的企业都在AI做大量布局,而不是一两家公司在赌,这说明AI的趋势确定性。

作为顶级的企业,你重注AI,你未必能够成功,但如果你不部署AI,你一定不成功。未来谁占里这个高地不知道,但是如果你不占领的话,也许很快会被被颠覆。

AI领域投资,我们看好三大方向

既然AI是刚需,大公司有都重注投入,那么从投资的角度,大企业都在部署AI,应该怎么去投呢?

我给一些自己的理解和建议:

行业AI

第一,是跟场景结合。

目前,在国外多出手纯技术类公司,不考虑短期商业模式,因为他们相对鼓励原创,有很强的技术辨别和趋势预测能力,而目前中国的投资还是以商业模式判断为主。

所以在中国,AI企业要想存活,必须要跟一些场景结合。在投资AI企业的时候,建议去找一些传统行业,AI只是一个技术,我们要找那些行业里面做得比较好的,创始人可能不是做AI的,但他用到了AI,而且AI给产生很好的效果,这种公司值得我们去关注。这种公司有可能通过这一轮技术创新,对整个行业某种程度上进行革新和洗牌。所以不要为了AI去看AI,你要去行业里面看AI的因素是不是很强,真的帮企业提高效率,产生更大的价值。

第二,是利用AI帮企业赋能,降低企业使用AI的门槛。

这些企业主要是技术驱动,虽然没有直接的场景,但是能在不同垂直行业赋能,或者帮企业降低使用AI的门槛。因为AI要用好它,对企业还是有较高门槛的,首先要有数据的存储和分布,把它变成一个分布式系统。其次,还得懂算法和业务,算法和业务结合通常要花较多时间成本。第三,最重要的是工程实现。如果你没有做过,会趟无数的坑。这层面的人才都集中在之前拥有大量数据的大公司,人才很难获得。所以现在市面上出现一些做AI的中间件,让企业接近傻瓜式的使用AI。比如做物流,需要大规模路径规划,只要告诉目标,提供数据源,可以直接帮企业优化这个业务。华映最近投资的天云大数据,就是做AI paas平台,帮助企业降低AI的使用门槛。

第三,底层技术也值得关注,譬如算法芯片。

任何算法的硬件实现都有一定的局限性,很能适用所有的算法,所以大公司不会那么激进,这就给初创公司很好的从边缘切入中心的机会。

以上就是我对人工智能发展的一些观点,再次感谢!


          Google AI with Jeff Dean      Cache   Translate Page      

Jeff Dean, the lead of Google AI, is on the podcast this week to talk with Melanie and Mark about AI and machine learning research, his upcoming talk at Deep Learning Indaba and his educational pursuit of parallel processing and computer systems was how his career path got him into AI. We covered topics from his team’s work with TPUs and TensorFlow, the impact computer vision and speech recognition is having on AI advancements and how simulations are being used to help advance science in areas like quantum chemistry. We also discussed his passion for the development of AI talent in the content of Africa and the opening of Google AI Ghana. It’s a full episode where we cover a lot of ground. One piece of advice he left us with, “the way to do interesting things is to partner with people who know things you don’t.”

Listen for the end of the podcast where our colleague, Gabe Weiss, helps us answer the question of the week about how to get data from IoT core to display in real time on a web front end.

Jeff Dean

Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow, leading Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.

Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.

Cool things of the week
  • Google Dataset Search is in beta site
  • Expanding our Public Datasets for geospatial and ML-based analytics blog
    • Zip Code Tabulation Area (ZCTA) site
  • Google AI and Kaggle Inclusive Images Challenge site
  • We are rated in the top 100 technology podcasts on iTunes site
  • What makes TPUs fine-tuned for deep learning? blog
Interview
  • Jeff Dean on Google AI profile
  • Deep Learning Indaba site
  • Google AI site
  • Google AI in Ghana blog
  • Google Brain site
  • Google Cloud site
  • DeepMind site
  • Cloud TPU site
  • Google I/O Effective ML with Cloud TPUs video
  • Liquid cooling system article
  • DAWNBench Results site
  • Waymo (Alphabet’s Autonomous Car) site
  • DeepMind AlphaGo site
  • Open AI Dota 2 blog
  • Moustapha Cisse profile
  • Sanjay Ghemawat profile
  • Neural Information Processing Systems Conference site
  • Previous Podcasts
    • GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li podcast
    • GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell podcast
    • TWiML & AI Systems and Software for ML at Scale with Jeff Dean podcast
  • Additional Resources
    • arXiv.org site
    • Chris Olah blog
    • Distill Journal site
    • Google’s Machine Learning Crash Course site
    • Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville book and site
    • NAE Grand Challenges for Engineering site
    • Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean paper and tweet
    • Machine Learning for Systems and Systems for Machine Learning slides
Question of the week

How do I get data from IoT core to display in real time on a web front end?

  • Building IoT Applications on Google Cloud video
  • MQTT site
  • Cloud Pub/Sub site
  • Cloud Functions site
  • Cloud Firestore site
Where can you find us next?

Melanie is at Deep Learning Indaba and Mark is at Tokyo NEXT. We’ll both be at Strangeloop end of the month.

Gabe will be at Cloud Next London and the IoT World Congress.


          KDnuggets™ News 18:n34, Sep 12: Essential Math for Data Science; 100 Days of Machine Learning Code; Drop Dropout      Cache   Translate Page      
Also: Neural Networks and Deep Learning: A Textbook; Don't Use Dropout in Convolutional Networks; Ultimate Guide to Getting Started with TensorFlow.
          Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server      Cache   Translate Page      

Here is How To Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server. It is probably easy to install Anaconda for Python packages.

The post Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server appeared first on The Customize Windows.


          Luca Massaron, Alberto Boschetti - TensorFlow Deep Learning Projects      Cache   Translate Page      
Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios. TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy.
          TensorFlow 推出数据验证函数库 TFDV,用于分析和验证      Cache   Translate Page      

TensorFlow 数据验证(TensorFlow Data Validation, TFDV)是一个用于探索与验证机器学习数据的函数库,特别设计为高度可扩展,用于验证以及监控机器学习的数据。

TensorFlow 产品经理 Clemens Mewald 表示,学术界和业界都非常关注机器学习的算法和性能,而数据是其中最重要的因素,一旦数据错误,所有相关的优化工作都将前功尽弃,因此数据整理是一项重要的工作 —— 通过对数据的理解以及验证来确保数据的正确性和可用性。在数据量较少的时候,可用人工的方式进行整理。但在实际应用中,研究人员使用的数据量往往非常庞大,这就给手动检查造成了巨大压力,用人工的方式进行数据验证有点不切实际。因此有必要使用自动化和可扩展的数据分析、验证和监控。

TFDV 是 TFX 平台的一部分,它也是 Google 每天用来分析和验证 PB 级数据的技术。鉴于此前它在数据纠错上一直有不错的表现,Google 相信,TFDV 也可以被用户作为维持 ML 模型性能的一个好工具。事实上,在设计 TFDV 的早期,Google 就已经考虑到了在笔记本电脑环境中使用它的需求,所以对于硬件要求,各位大可不必担心。

在正式的生产环境中使用 TFDV,也是使用和在笔记本电脑环境相同的函数库,以进行大规模的数据分析和验证,不过有一些特殊的使用案例,包括检测连续版本训练数据之间的分布变化,以及检查训练数据和服务系统观察到的数据之间的特征值/分布差异。

目前官方已在 GitHub 上开源 TFDV(https://github.com/tensorflow/data-validation),其中包括用于笔记本电脑环境的示例代码。另外官方也提供了端到端的示例,展示了 TFDV 与 TensorFlow Transform、TensorFlow Estimators、TensorFlow Model Analysis 和 TensorFlow Serving 一起使用的方法。


          PYTHON Developer - Klein Management Systems - San Jose, CA      Cache   Translate Page      
Work on the design, implementation, technical support and evaluation of new and existing systems. Machine Learning with Python, Tensorflow, SyntaxNet and R...
From Klein Management Systems - Thu, 09 Aug 2018 17:29:34 GMT - View all San Jose, CA jobs
          Create enhanced animated 3D models with realistic features      Cache   Translate Page      
I want you to build me 3D models with animation. I want you to use Deep Learning and Tensorflow to enhance the realistic features. The model should be as detailed as possible (realistic features). Inbox... (Budget: $30 - $250 USD, Jobs: 3D Animation, 3D Modelling, 3D Rendering, Machine Learning, Tensorflow)
          Create enhanced animated 3D models with realistic features      Cache   Translate Page      
I want you to build me 3D models with animation. I want you to use Deep Learning and Tensorflow to enhance the realistic features. The model should be as detailed as possible (realistic features). Inbox... (Budget: $30 - $250 USD, Jobs: 3D Animation, 3D Modelling, 3D Rendering, Machine Learning, Tensorflow)
          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server      Cache   Translate Page      

Here is How To Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server. It is probably easy to install Anaconda for Python packages.

The post Install Jupyter Notebook and TensorFlow On Ubuntu 18.04 Server appeared first on The Customize Windows.


          【Python】keras神经网络识别mnist      Cache   Translate Page      

上次用Matlab写过一个识别Mnist的神经网络,地址在: https://www.cnblogs.com/tiandsp/p/9042908.html

这次又用Keras做了一个差不多的,毕竟,现在最流行的项目都是python做的,我也跟一下潮流:)

数据是从本地解析好的图像和标签载入的。

神经网络有两个隐含层,都有512个节点。


import numpy as np
from keras.preprocessing import image
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
# 从文件夹图像与标签文件载入数据
def create_x(filenum, file_dir):
train_x = []
for i in range(filenum):
img = image.load_img(file_dir + str(i) + ".bmp", target_size=(28, 28))
img = img.convert('L')
x = image.img_to_array(img)
train_x.append(x)
train_x = np.array(train_x)
train_x = train_x.astype('float32')
train_x /= 255
return train_x
def create_y(classes, filename):
train_y = []
file = open(filename, "r")
for line in file.readlines():
tmp = []
for j in range(classes):
if j == int(line):
tmp.append(1)
else:
tmp.append(0)
train_y.append(tmp)
file.close()
train_y = np.array(train_y).astype('float32')
return train_y
classes = 10
X_train = create_x(55000, './train/')
X_test = create_x(10000, './test/')
X_train = X_train.reshape(X_train.shape[0], 784)
X_test = X_test.reshape(X_test.shape[0], 784)
Y_train = create_y(classes, 'train.txt')
Y_test = create_y(classes, 'test.txt')
# 从网络下载的数据集直接解析数据
'''
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, Y_train = mnist.train.images, mnist.train.labels
X_test, Y_test = mnist.test.images, mnist.test.labels
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train.reshape(55000, 784)
X_test = X_test.reshape(10000, 784)
'''
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.4))
model.add(Dense(10))
model.add(Activation('softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = model.fit(X_train, Y_train, batch_size=500, epochs=20, verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
test_result = model.predict(X_test)
result = np.argmax(test_result, axis = 1)
print(result)
print('Test score:', score[0])
print('Test accuracy:', score[1])

最终在测试集上识别率在98%左右。


【Python】keras神经网络识别mnist

相关测试数据可以在这里 下载 到。


          TuxMachines: today's leftovers      Cache   Translate Page      
  • Have You Ever Considered Replacing Windows with Linux? [Ed: Microsoft propagandist (for over a decade) Bogdan Popa continues to provoke GNU/Linux users]
  • Windows file sharing comes to Chromebooks

    You can run Android apps on Chromebooks. You can run Linux programs on Chromebooks. Heck, you can even run Windows programs on Chromebooks. But one thing you couldn't do natively on a Chromebook is read and write files on a Windows PCs or Windows and Samba servers. Things change. With the forthcoming release of Chrome OS 70, you can access network file shares from Chromebooks.

    To do this, once Chrome OS 70 is available to all users, open Settings, look for "Network File Shares", click the "Add File Share" button, and enter your user name and password. Then, click "Add" button and open the Files app to browse your newly mounted shared folder. That's all there is to it.

  • 5 examples of Prometheus monitoring success

    Prometheus is an open source monitoring and alerting toolkit for containers and microservices. The project is a hit with lots of different organizations regardless of their size or industrial sector. The toolkit is highly customizable and designed to deliver rich metrics without creating a drag on system performance. Based on the organizations that have adopted it, Prometheus has become the mainstream, open source monitoring tool of choice for those that lean heavily on containers and microservices.

    Conceived at SoundCloud in 2012, Prometheus became part of the Cloud Native Computing Foundation (CNCF) in 2016 and in August 2018, CNCF announced Prometheus was the second "graduated" project in the organization's history.

    Prometheus provides a key component for a modern DevOps workflow: keeping watch over cloud-native applications and infrastructure, including another popular CNCF project, Kubernetes.

  • Unique RTS game 'Circle Empires' to get Linux support later this month

    Publisher Iceberg Interactive sent word today that the unique RTS game Circle Empires from developer Luminous is heading to Linux. They didn't give an exact date other than "Later this month Circle Empires will also receive full Linux support.".

    Since I'm a big fan of RTS games, I was instantly quite surprised with how Circle Empires works. The map is literally split into circles, with you battling for control of each one of them.

  • Timespinner, a metroidvania featuring time travel, is set to be released September 25th

    Fans of metroidvanias will be getting a new game to sink their teeth into soon enough. A new trailer shows off what you can expect from the story and gameplay.

  • TensorFlow on Debian/sid (including Keras via R)

    I have been struggling with getting TensorFlow running on Debian/sid for quite some time. The main problem is that the CUDA libraries installed by Debian are CUDA 9.1 based, and the precompiled pip installable TensorFlow packages require CUDA 9.0 which resulted in an unusable installation. But finally I got around and found all the pieces.

  • Skylake mini-PC has dual M.2 slots and up to 32GB DDR4

    Aaeon has launched a Linux-ready “Nano-002N” mini-PC with a 6th Gen Core CPU, up to 32GB DDR4, 2x GbE, 2x HDMI, and 4x USB 3.0 ports, plus dual M.2 slots.

    Aaeon’s Nano-002N upgrades its Intel 5th Gen Nano-001N from 2015 with a dual-core, 6th Gen “Skylake” U-series CPU and additional new features. These include a serial port and twice the maximum memory for up to 32GB DDR4, among other enhancements. The mini-PC is well suited for media player, digital signage and POS, as well as other “tough applications in the factory, office, and off-site locations.”

read more


          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          微軟釋出ML.NET 0.5,開始支援深度學習TensorFlow模型      Cache   Translate Page      
微軟在5月釋出由微軟研究院開發、發展了十年的機器學習框架ML.NET,今釋出了ML.NET 0.5,最大的更新便是開始支援TensorFlow,開發者可以在ML.NET中直接使用已經訓練好的TensorFlow模型,進行評分(Scoring)。另外,微軟正在開發新的ML.NET API,屆時將會棄用現行的LearningPipeline API。
          Google introduces machine learning analysis tool to combat AI bias      Cache   Translate Page      
Google has unveiled a bias-detection feature for its TensorFlow machine learning web application, dubbed the What-If Tool, in a blog...
          A Proposal to Get Rid of 'node_modules'      Cache   Translate Page      

#255 — September 13, 2018

Read on the Web

Node Weekly

Next Generation Package Management with Cruxcrux is a new, experimental JavaScript package manager from the folks at npm, Inc, that aims to provoke new thoughts on how package management should be handled.

The npm Blog

Node v10.10.0 (Current) Released — npm moves up to version 6.4.1, native code coverage information can now be saved to disk, the http2 module is no longer experimental, and much more. Node 8.12.0 (LTS) is also out which also updates npm, libuv, and makes n-api non-experimental.

Node.js Foundation

Burn Your Logs — Use Sentry's open source error tracking to get to the root cause of issues. Setup only takes 5 minutes.

Sentry sponsor

Debugging A Node.js Application Using ndb — ndb provides an improved debugging experience for Node.js, enabled by Chrome DevTools, and this is an easily understood walkthrough.

Nitay Neeman

A Proposal to Get Rid of 'node_modules' — It’s early days for this discussion but there’s a lot of chatter about this right now (such as on Hacker News). Full PDF of the proposal.

Yarn

NLP.js: Natural Language Utilities for Node — An NLP library that can guess the language of a phrase, do stemming/tokenization, sentiment analysis, and more.

AXA

💻 Jobs

Senior Engineer, LA — At SG Sr. Engineers build both customer facing solutions to drive engagement and internal tools to support restaurant operations.

sweetgreen

Join Our Career Marketplace & Get Matched With a Job You Love — Through Hired, software engineers have transparency into salary offers, competing opportunities and job details.

Hired

📘 Articles & Tutorials

8 Steps to Building A Serverless GraphQL API using AWS Amplify

Nader Dabit

How to Prevent Unsafe HTTP Redirects in Node

Joe Pelletier

Build a Netflix Style Video Platform - Node API Client — Play videos at the same quality and speed as Netflix & YouTube. API clients for all major languages.

Bitmovin sponsor

Add 2FA to a Nuxt Application with Nexmo VerifyNuxt.js is a framework for building universal Vue.js apps.

Martyn Davies

Defining Roles-based Security ACLs and Supporting Multitenancy in the Strongloop Loopback Framework

Steve Drucker

Generating Random User Agents with Google Analytics and CircleCIuser-agents is a Node.js package for producing random, up to date user agents, but this is also the tale of how such data is being obtained.

Evan Sangaline

▶  Building a Real-Time Translation App From Scratch — Re-watch this livestream and code along, making a real-time translation app from scratch using Node and Tensorflow.js.

Siraj Raval

CPU Profiling in Production Node.js Applications

StackImpact sponsor

Why Should Your Node App Not Handle Log Routing?

Corey Cleary

🔧 Code and Tools

User Agents: A Library for Generating Random, But Real-Looking, User Agents

Intoli

Taiko: A Library and REPL to Automate Chrome/Chromium — Includes a REPL mode and is more designed to work with a visible, rather than headless, browser instance.

Gauge

Express.js Boilerplate for Building RESTful APIs — A starter project to build a REST-based API service with Node.js that uses MongoDB for storage.

Daniel Sousa

Drome: Yet Another JavaScript Task Runner

Konrad Przydział

Puppeteer 1.8.0 Released: The Headless Chrome Node API — The latest release operates at Chromium 71 standards and browser permissions can now be managed with browserContext.overridePermissions.

Google Chrome Team


          Open Sourcing TonY: Native Support of TensorFlow on Hadoop      Cache   Translate Page      
Co-authors: Jonathan Hung, Keqiu Hu, and Anthony Hsu LinkedIn heavily relies on artificial intelligence to deliver content and create economic opportunities for its 575+ million members. Following recent rapid advances of deep learning technologies, our AI engineers have started adopting deep neural networks in LinkedIn’s relevance-driven products, including feeds and smart-replies. Many of these use cases are built on TensorFlow, a popular deep learning framework written by Google. In the beginning, our internal TensorFlow users ran the framework on small and unmanaged “bare metal” […]
          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          LinkedIn open sources TonY, its framework to run TensorFlow on Hadoop      Cache   Translate Page      
The core idea is to run TensorFlow jobs as reliably and flexibly as other first-class citizens on Hadoop.
          R Deep Learning Essentials      Cache   Translate Page      

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.


          Technical Writer II - Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Frameworks and Infrastructure with tools like Apache MXNet and TensorFlow, API-driven Services like Amazon Lex, Amazon Polly and Amazon Rekognition to quickly...
From Amazon.com - Mon, 13 Aug 2018 19:22:01 GMT - View all Seattle, WA jobs
          LinkedIn open sources TonY, its framework to run TensorFlow on Hadoop      Cache   Translate Page      
The core idea is to run TensorFlow jobs as reliably and flexibly as other first-class citizens on Hadoop.

          Python and tensorflow expert      Cache   Translate Page      
Help in small tensorflow and python project (Budget: $30 - $50 AUD, Jobs: Machine Learning, Python, Software Architecture, Tensorflow)
          Google introduces machine learning analysis tool to combat AI bias      Cache   Translate Page      
Google has unveiled a bias-detection feature for its TensorFlow machine learning web application, dubbed the What-If Tool, in a blog post. The What-If ...
          Python and tensorflow expert      Cache   Translate Page      
Help in small tensorflow and python project (Budget: $30 - $50 AUD, Jobs: Machine Learning, Python, Software Architecture, Tensorflow)
          Comment on TensorFlow JS Tutorial – Build a neural network with TensorFlow for Beginners by Infoundation Organisation      Cache   Translate Page      
Any help in Text Classification in tensorflow.js
          Some modifications about SSD-Tensorflow      Cache   Translate Page      

In theprevious article, I introduced a new library for Object Detection. But yesterday, after I added slim.batch_norm() into ‘nets/ssd_vgg_512.py’ like this:

def ssd_arg_scope(weight_decay=0.0005, data_format='NHWC', is_training=False):
...
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        activation_fn=tf.nn.relu,
                        weights_regularizer=slim.l2_regularizer(weight_decay),
                        weights_initializer=tf.contrib.layers.xavier_initializer(),
                        biases_initializer=tf.zeros_initializer(),
                        normalizer_fn=slim.batch_norm):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                            padding='SAME',
                            data_format=data_format):
            with slim.arg_scope([custom_layers.pad2d,
                                 custom_layers.l2_normalization,
                                 custom_layers.channel_to_last],
                                 data_format=data_format):
                with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training) as sc:
                    return sc

Although training could still run correctly, the evaluation reported errors:

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [128] rhs shape= [256]
         [[Node: save/Assign_112 = Assign[T=DT_FLOAT, _class=["loc:@ssd_512_vgg/conv2/conv2_2/BatchNorm/moving_variance"], use_locking=true, validate_
shape=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ssd_512_vgg/conv2/conv2_2/BatchNorm/moving_variance, save/RestoreV2/_283)]]

I wondered why adding some simple batch_norm will make shape incorrect for quite a while. Finally I findthis pagefrom google. It said this type of error is usually made by incorrect data_format setting. Then I check the code of ‘train_ssd_network.py’ and ‘eval_ssd_network.py’, and got the answer: the training code use ‘NCHW’ but evaluating code use ‘NHWC’!
After changing data_format to ‘NCHW’ in ‘eval_ssd_network.py’, the evaluation script runs successfully.

0     0

udpwork.com 聚合 | 评论: 0 | 要! 要! 即刻! Now!


          Google introduces machine learning analysis tool to combat AI bias      Cache   Translate Page      
Google has unveiled a bias-detection feature for its TensorFlow machine learning web application, dubbed the What-If Tool, in a blog...
          Linkedin Open Source Tool to Deploy TensorFlow on Hadoop      Cache   Translate Page      
Depositphotos_38337841_s-2015

LinkedIn tool reduces the amount of time required to create AI models by making massive amounts of data stored in Hadoop more accessible.

The post Linkedin Open Source Tool to Deploy TensorFlow on Hadoop appeared first on RTInsights.




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