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Skymind - Udacity China presentation
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Adam Gibson
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Recent presentation I gave with Udacity In china on deep learning applications.
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Skymind - Udacity China presentation
1.
深度学习 挖掘大数据价值,展现大数据魅力,构建大数据应用 Skymind 的合作伙伴:
2.
深度学习: 概述 深度学习是机器学习研究中的一 个新的领域,其动机在于建 立、模拟人脑进行分析学习 的神经网络 深度学习技术大大提高了计算的 精度与准确率。 能识别,分析并学习文字,图片 ,声音,视频以及时间序列 数据。 未来计算机发展的必然趋势
3.
深度学习: 概述
4.
深度学习: 需求 首席技术官(CTO)寻找在机器学习上的突 破 数据科学家(Data Scientist)
处理大量的 非机构数据时,不再费力的去分析数据的特 征 企业(Business) 将大数据的潜在价值转 化为实际利益
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深度学习 深度学习让机械可以像人一样听懂,看懂、分析和思考
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深度学习: 大数据时代 在数据分析中,有超过90%数据都是来自于非结构化数据,其中大部分的是日志,如运维、安全审计 、用户访问数据以及业务数据等,但随着互联网快速的发展,数据规模也是水涨船高,从早前的GB 级到现在的TB级,甚至PB级也只是短短几年光景。
7.
深度学习 就像人一样,随着时间慢慢的累积经验和知识,判断和分析能力也随之增强 深度学习平台的优势在于它超高的精准度和超快 的运行速度
8.
深度学习用列:图像识别 近年来,深度学习在图像和视频分析领域的应用取得了巨大的成功 深度学习在图像和视频分析领域的应用取得了巨大的成功。即便是像人脸如此复杂的图像,使用深度 学习技术也可达到99.47%的识别率。深度学习在图像识别中的应用方兴未艾,在不同的行业上都有 着巨大的发展空间。
9.
深度学习用列:人脸识别 通过深度学习,人脸识别技术迎来了春天,在身份验证、国防安全领域,发挥了巨大的作用 经典的人脸识别算法Eigenface在 LFW 中的识别率只有 60%,而深度学习算法 的识别率是
99.47%,甚至超过了人眼 在此测试集的识别率(99.25%)。 除了能识别人脸,深度学习还可以分析 人脸的属性、特征与变化。 戴眼镜 红色口红 微笑 女生 褐色头发 闭眼 微妆 长发 痣 23岁
10.
深度学习用列:寻找盗车贼或遗失车辆 通过深度学习,除了能识别车牌,还可分辨车的各种特征 跟踪汽车 识别汽车所有的细节 ● 识别汽车的凹痕和刮痕 ● 识别汽车的型号 ●
识别车牌 ● 识别改装汽车
11.
深度学习用列:道路检测 通过Skymind深度学习,增强智能道路检测系统 使用深度学习分析面破损的性状,提高公路路面破损检测分析的效率于高精准的结果,可以减轻路面 养护与管理的人物 损坏程度:90% 需要维修 网状裂缝 龟裂 损坏程度:30% 损坏程度:0% 状态良好
12.
深度学习用列:反欺诈 根据中国互联网协会发布的数据,2013年就有至少1.2亿的网民遭遇各种网络欺诈,总共损至少1491.5亿元 深度学习可以学习、结合所有 信息并分析每个用户的生活规 律、消费习惯、买卖细节、黑 客的欺诈模式等 根据收集到的所有细节中分析 涉嫌欺诈几率,保护用户,防 止黑客欺诈。 欺诈侦查准确率 深度学习 一般侦查方式 95% 70% 根据真实案例研究,深度学习 大大提高了欺诈识别率(准确 率高达95%),把欺诈的损失 削减了一半!
13.
Skymind深度学习用列:反欺诈 根据中国互联网协会发布的数据,2013年就有至少1.2亿的网民遭遇各种网络欺诈,总共损至少1491.5亿元 法国电信集团 法国电信集团一直以来都试图阻止黑客盗用他们的网络来提供 便宜的电话服务。因为黑客的欺诈手法层出不穷,变化多端, 法国电信集团的研究部没法跟上黑客,无法使用原有的侦测器 来检测这些漏洞。 法国电信集团为了这事件使用了深度学习来减轻这问题。 使用了深度学习后,法国电信集团已在开始的阶段成功降低了 至少百分之五十的盗用损失。
14.
深度学习用列:硬件寿命检测 有效的利用现有资源,掌握硬件健康,预防硬件损坏造成的致命损害,延长硬件寿命 硬件温度 硬件运行速度 硬件旋转率 硬件读取率 室外气温 硬件发出的声音 使用量 电源信息 硬件应用 错误率 磁场信息 硬件性能 硬件寿命 每个硬件都存有大量重要的信息,而这些信息往往会被人忽略,或因为这些数据量都太大导致人类难 以用来分析硬件的寿命及性能。Skymind深度学习可以分析这些大数据,自动发现硬件损坏特征,进 而可以有效的预测硬件寿命及性能。
15.
深度学习用列:自动驾驶 近年来,深度学习在图像和视频分析领域的应用取得了巨大的成功
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深度学习用列:机械工人 亚马逊(Amazon )部署了10,000 机器人来搬运货物
17.
深度学习用列:无人驾驶飞机 近年来,深度学习在图像和视频分析领域的应用取得了巨大的成功 驾驶方向 向后 向前 向左
向右 升高 降低
18.
Skymind深度学习用列:推荐系统 深度学习:个性化推荐系统持续优化的奥秘
19.
Skymind深度学习用列:自然语言处理 通过多层神经网络,深度学习的应用已经在自然语言处理领域成功地产生了诸多突破性的成果 1. 语音和图像在处理过程中的输入信号可 以在向量空间内表示,而自然语言处理 通常在词汇一级进行。将独立的词语转 换为向量并作为神经网络的输入是将神 经网络应用于自然语言处理的基础。 2. 自然语言任务中通常要处理各种递归结 构。语言模型、词性标注等需要对序列 进行处理,而句法分析、机器翻译等则 对应更加复杂的树形结构。这种结构化 的处理通常需要特殊的神经网络。
20.
深度学习用列:癌症检测 用深度学习让超声诊断系统有了重大的技术进展,让机器替代人类进行癌症识别
21.
深度学习用列:疾病侦查 用深度学习让超声诊断系统有了重大的技术进展,让机器替代人类进行癌症识别 医学博士,Regenstrief生物医学信息学的中 心临时主任Shaun Grannis说:“我们认为, 利用深度学习技术,人类不再需要花时间审 阅文本报告来确定患者是否患有疾病。”
22.
深度学习用列:阿尔法围棋(AlphaGo) 利用 “价值网络” 去计算局面,用
“策略网络” 去选择下子
23.
深度学习:想成为深度学习专家? 这些都是成为深度学习专家的要素 1. 对机器学习有深度的理解 2. 懂得Java,Scala,Python,C/C++
编辑 语言 3. 会实现一个神经网络,并利用它建立机器 学习模型 4. 理解神经网络的原理,针对不同的问题使 用不同的神经网络 5. 用深度学习来解决各种各样的难题了,比 如面部识别、语音识别和自动驾驶等 到 deeplearning4j.org 去使用深度学习建立 你第一个机器学习模型
24.
深度学习讨论区: https://gitter.im/deeplearning4j 问答讨论
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