请联系主办方进行认证,即可解锁访问限制。
为了不影响召集报名,请您进行认证,即可解锁访问限制。
OReilly和Intel人工智能大会2019北京站
该主办方未认证,请注意风险防范!
{{list.startDate}} ~ {{list.overDate}}
{{list.overDate}}结束
{{list.startDate}}开始
票种
-
免费 ¥{{toDecimal2(item.price)}} {{item.name}} ¥{{ toDecimal2(item.plusPrice) }} 优惠码减免¥{{item.discountMoney}} 优惠码折扣{{item.discountRate}}%
-
免费 ¥{{toDecimal2(item.price)}} {{item.name}} ¥{{ toDecimal2(item.plusPrice) }} 优惠码减免¥{{item.discountMoney}} 优惠码折扣{{item.discountRate}}%
{{item_time_note}} {{ticketText != ''&&item_time_note!=''?'(':''}} 说明:{{ticketText}} {{ticketText != ''&&item_time_note!=''?')':''}}
数量
领券
-
立减{{coupon.couponDiscountMoney}}元
满{{coupon.couponLimitMoney}}减{{coupon.couponDiscountMoney}}
该主办方未认证,请注意风险防范!
互动吧
{{pub_count}}
活动{{fansCount}}
粉丝{{shopDesc|html}}进店 >
Ta组织活动太忙,还没腾出空写简介进店 >
人工智能大会把硅谷带到中国
人工智能北京大会是无与伦比的世界先锋创新者盛会。极度聚焦于技术内容和商业应用的交融,吸引了世界各地的热爱人工智能人士。大会有4天信息满满的内容,包括实用性的分会场议题,深度培训课程,极具启发性的主题演讲,以及难得的思想交流与碰撞的社交机会。
人工智能大会:将人工智能在工作中用起来
本次大会的独特之处在于将重点放在应用人工智能——弥合人工智能研究领域与产业商业应用之间的差距。
只有本次北京人工智能大会才将硅谷和中国融合在一起,创造一次全球人工智能专家难得的相聚。讲师为来自各公司人工智能专家,包括百度、谷歌、eBay、Bonsai、Uber、微软、阿里巴巴、亚马逊、SAS、Unity、SalesForce、IBM、伯克利、斯坦福及牛津大学——仅为部分公司。
无论你的关注点在哪里都将在本次人工智能大会上找到:
企业中的人工智能:执行简报,案例研究及用例,行业特定应用
人工智能对商业及社会的影响:自动化,安全,规范
实施人工智能项目:应用,工具,架构,安全
与人工智能交互:设计,指标,产品管理,机器人
模型及方法:增强及机器学习,TensorFlow,深度学习,GAN,自然语言处理及理解,语音识别,计算机视觉
人工智能培训课程
将自己沉浸在两天针对关键主题的课程中。培训课程安排在6月18-19日进行,控制班级规模以保证参会者的学习体验(包括与讲师互动)。
课程一 量化互联网金融信用与反欺诈风控
课程二 Deep Learning with TensorFlow
课程三 Deep Learning with PyTorch
课程四 Professional Kafka development
会议精彩内容节选
Deep Learning with PyTorch
Rich Ott (The Data Incubator)
PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy to use API and seamless use of GPUs make it a sought after tool for deep learning. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets.
Deep Learning with TensorFlow
Season Yang (McKinsey & Company)
The
TensorFlow library provides for the use of computational graphs, with
automatic parallelization across resources. This architecture is ideal
for implementing neural networks. This training will introduce
TensorFlow's capabilities in Python. It will move from building machine
learning algorithms piece by piece to using the Keras API provided by
TensorFlow with several hands-on applications.
量化互联网金融信用与反欺诈风控
Jike Chong (Tsinghua University | Acorns)
黄铃 (Tsinghua University)
陈薇 (排列科技)
您想了解金融企业是怎样利用大数据和人工智能技术来画像个人行为并检测欺诈用户的吗?互联网金融幕后的量化分析流程是怎么杨的?个人信用是怎样通过大数据被量化的?在实践过程中,机器学习算法的应用存在着哪些需要关注的方面?怎样通过图谱分析来融合多维数据,为我们区分正常用户和欺诈用户?
这套辅导课基于清华大学交叉信息研究院开设的一门"量化金融信用与风控分析”研究生课。其中会用LendingClub的真实借贷数据做为案例,解说一些具体模型的实现。
A practical guide towards explainability and bias Evaluation in machine learning
Alejandro Saucedo (The Institute for Ethical Ai & Machine Learning)
Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We'll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our machine learning models.
Design thinking for AI
Chris Butler (Philosophie)
Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.
基于深度学习的时间序列预测 (Deep learning for time series forecasting)
Yijing Chen (Microsoft)
Dmitry Pechyoni (Microsoft)
Angus Taylor (Microsoft)
Vanja Paunic (Microsoft)
Henry Zeng (Microsoft)
Almost every business today uses forecasting to make better decisions and allocate resources more effectively. Deep learning has achieved a lot of success in computer vision, text and speech processing, but has only recently been applied to time series forecasting. In this tutorial we show how and when to apply deep neural networks to time series forecasting. The tutorial will be in CHN and EN.
云服务加速人工智能创新(Accelerate innovations with AI in the cloud)
Long Wang (Tencent)
We all know that Cloud is the best place to use new technologies. Long Wang examines what's happening for AI in the cloud. How does AI in the cloud accelerate the innovations in the industry? What's mostly possible? What's still on the way? How does cloud help?
Building reinforcement learning models and AI applications with Ray
Richard Liaw (UC Berkeley RISELab)
Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.
领英基于Spark和TensorFlow的大规模AI基础架构
Min Shen (LinkedIn)
领英公司的几乎所有产品都离不开基于海量数据和大规模数据运算的机器学习模型。怎样构建一个稳定,高效,和易用的人工智能基础架构,越来越成为一个核心的问题。 这个演讲会先介绍领英大数据团队是怎样在5年的时间里演进这个基础架构,从开始的完全基于Spark的系统,到现在Spark+TensorFlow的环境。 我们还会重点介绍近期解决的技术挑战,来应对接近500PB数据和将近6亿会员的巨大经济图谱。这些挑战包括大规模重量级的深度学习模型,Spark的调优,以及在机器学习生产线中连接不同的步骤(数据准备,模型构建,模型训练,在线inference)。 最后我们会介绍我们近期一些成功的深度学习案例,以及团队在AI基础架构上未来2~3年的计划和愿景。
Efficient deep learning for the edge
Bichen Wu (UC Berkeley)
The success of deep neural networks is attributed to three factors: stronger computing capacity, more complex neural networks, and more data. These factors, however, are usually not available with the edge applications as autonomous driving, AR/VR, IoT, and so on. In this talk we discuss how we apply AutoML, SW/HW codesign, domain adaptation to solve these problems.
The Future of Machine Learning is Tiny
Pete Warden (Google)
There are over 250 billion embedded devices in the world. On-device machine learning gives us the ability to turn wasted data into actionable information, and will enable a massive number of new applications over the next few years. Pete Warden digs into why embedded machine learning is so important, how it can be implemented on existing chips, and some of the new uses it will unlock.
Hacking humans made easy: Signal processing + AI + video
David Maman (Binah.ai)
Zero-day attacks. IoT-based botnets. Cybercriminal AI v. cyberdefender AI. While these won’t be going away, they aren’t the biggest worry we have in cybercrime. Hacking humans is. The combination of mere minutes of video, signal processing, remote heart rate monitoring, AI, machine learning, and data science can identify a person’s health vulnerabilities, which evildoers can make worse.
Exciting new features in TensorFlow 2.0
Tiezhen Wang (Google)
TensorFlow 2.0 is a major milestone with a focus on ease of use. This talk will give a in depth introduction to the new exciting features and best practices. Topics such as distributed strategies and edge deployment (TensorFlow Lite and TensorFlow.js) will also be covered.
自动机器学习(Automated machine learning)技术的实践与应用
Hui Xue (微软亚洲研究院)
人工智能在过去的几年里飞速发展,但是机器学习的实践和应用需要消耗一定的人力和时间。例如,如何去做特征选择,如何设计一个适合该任务的神经网络模型等等。而自动机器学习技术,可以帮助开发者和机器学习实战者,缩短开发周期,提高效率。我们的介绍主要包括:自动机器学习技术的进展;我们开源的自动机器学习开源库Neural Network Intelligence; 如何利用自动机器学习的技术,在产品和应用上提高效率,节省所需的时间和缩短周期。我们会在最后一部分,分享一些利用自动特征选择,自动参数调整以及模型架构搜索上的成功案例。
The unreasonable effectiveness of transfer learning on natural language processing
David Low (Pand.ai)
Transfer Learning has been proven to be a tremendous success in the Computer Vision field as a result of ImageNet competition. In the past months, the Natural Language Processing field has witnessed several breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit and BERT. In this talk, David will be showcasing the use of transfer learning on NLP application with SOTA accuracy.
The future of machine learning is decentralized
Alex Ingerman (Google)
Federated Learning is the approach of training ML models across a fleet of participating devices, without collecting their data in a central location. Alex Ingerman introduces Federated Learning, compares the traditional and federated ML workflows, and explores the current and upcoming use cases for decentralized machine learning, with examples from Google's deployment of this technology.
AI and Systems at RISELab
Ion Stoica (UC Berkeley)
In this talk, I will describe a few projects at the intersection of AI and Systems that we are developing at RISELab, UC Berkeley. The RISELab is the successor of AMPLab, where several highly successful open source projects, including Apache Spark and Apache Mesos, were de
Bringing research and production together with PyTorch 1.0
Joseph Spisak (Facebook)
Learn how PyTorch 1.0 enables you to take state-of-the-art research and deploy it quickly at scale in areas from autonomous vehicles to medical imaging. We'll deep dive on the latest updates to the PyTorch framework including TorchScript and the JIT compiler, deployment support, the C++ interface. We will also cover how PyTorch 1.0 is utilized at Facebook to power AI across a variety of products.
AI and retail
Mikio Braun (Zalando SE)
Taking
a look at Zalando and the retail industry we will explore how AI is
redefining the way e-commerce sites interact with the customer to create
a personalized experience that strives to make sure customers will find
what they want when they need it.
Designing Computer Hardware for Artificial Intelligence
Michael James (Cerebras)
Artificial Intelligence is defining a new generation of computer technology with applications that blur boundaries between intuition, art, and science. We will discuss the fundamental drivers of computer technology, survey the landscape of AI hardware solutions, and explore the limits of what is possible as new computer platforms emerge.
为什么说人工智能和云计算乃天作之合?(Why do we say AI Should be Cloud Native?)
Yangqing Jia (Facebook)
The recent years of AI has grown out of labs and created a transformative power for a vast range of industries. But, while we take it for granted that AI and Cloud come hand in hand, I'll show you an argument one step further: AI should be Cloud Native.
更多精彩议题内容可搜索AI大会或人工智能大会,进入官网查看详情:https://ai.oreilly.com.cn/ai-cn
分享到:
微信扫一扫,分享小程序
扫一扫,分享至朋友圈
温馨提示:
在付费报名之前请仔细甄别主办方的资质及服务能力。部分主办方会私下与报名者沟通承诺参与活动后的权益,并夸大参与后的收益效果等,以此来收取高额的报名费。这类活动通常有基于抖音、淘宝等平台的推广、代理加盟、引流变现等相关内容。
为保障您的权益,避免相关的经济损失,互动吧平台特此说明,平台仅提供相关的技术支持,不承担参与者与主办方在活动过程中的相关纠纷,若出现相关纠纷,平台会积极协助处理。
- 为你推荐
-
{{hot.infoStartTime}}
{{hot.infoStartTime}}
{{hot.infoStartTime.substr(0,16).replace(new Date().getFullYear()+'-','')}}
Live{{hot.plusDiscountPriceRange}}{{hot.priceWithSign}} {{hot.highlight|html}}
加载中
该主办方未认证,请注意风险防范!
{{pub_count}}
活动{{fansCount}}
粉丝{{shopDesc|html}}进店>
Ta组织活动太忙,还没腾出空写简介进店>
一对一为您答疑解惑
-
{{selectlist.title}}
{{selectlist.infoDate}}{{selectlist.priceWithSign}} {{selectlist.plusDiscountPriceRange}} {{selectlist.highlight}}
-
{{list.shortName}}天{{list.desc1}}{{list.desc2}}
成为银牌会员
{{infoText}}
-
高端模板免费用
提升活动人气
-
活动排名加权
提升活动排名
-
去除报名页广告
提升活动报名效果
-
高端邀请海报
全场无限使用
-
活动优先审核
快人一步上架曝光
-
大额提现
限额提升4倍
-
报名渠道监测
掌握各渠道业绩
-
发布多场次活动
发布一次一劳永逸
-
免认证服务
免99元审核服务费
-
更多特权
敬请期待
马上开通
-
{{item.type}}
¥{{item.price}}/{{item.viewType}}
¥{{item.oriPrice}}/{{item.viewType}}
季卡、半年卡、年卡均已包含认证审核服务费,支持开具发票
使用微信或支付宝扫码完成支付
支付金额:¥{{selectGrItem.price}}/{{selectGrItem.viewType}}(已省¥{{selectGrItem.oriPrice - selectGrItem.price}})
购买成功
已购买{{orderName}}
支付金额:¥{{payMoney}}
购买商品:{{orderName}}
扫码支付更轻松
购买成功
已购买{{orderName}}
{{curMemberData.title}}
{{curMemberData.tip}}
-
{{item.name}}
查看更多权益>
{{curMemberData.tags[0].name}}
查看更多权益>
{{item.imgText}}
- {{temp.text}}
购买成功
您已成功购买{{checkMemberData.name}}
扫码