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将人工智能用起来
2019年6月18-21日
北京,中国
 
多功能厅6A+B (Function Room 6A+B)
Add Deep learning with PyTorch to your personal schedule
09:00 Deep learning with PyTorch Rich Ott (The Data Incubator)
多功能厅5A+B(Function Room 5A+B)
Add Deep learning with TensorFlow to your personal schedule
09:00 Deep learning with TensorFlow Season Yang (McKinsey & Company)
多功能厅2(Function Room 2)
Add 量化互联网金融信用与反欺诈风控 to your personal schedule
09:00 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)
10:30 Morning Break | Room: 1st Floor Foyer
15:00 Afternoon Break | Room: 1st Floor Foyer
12:30 Lunch | Room: 彩虹厅 (Rainbow Room)
09:00-17:00 (8h)
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. Rich Ott introduces you to the PyTorch workflow and explores how its easy-to-use API and seamless use of GPUs makes it a sought-after tool for deep learning. Join in to get the knowledge you need to build deep learning models using real-world datasets.
09:00-17:00 (8h) 与人工智能交互 (Interacting with AI)
Deep learning with TensorFlow
Season Yang (McKinsey & Company)
The TensorFlow library contains computational graphs with automatic parallelization across resources, which is ideal architecture for implementing neural networks. Season Yang introduces TensorFlow's capabilities in Python, and you'll then get your hands dirty building machine learning algorithms piece by piece while using the Keras API provided by TensorFlow with several hands-on applications.
09:00-17:00 (8h) 实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
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。
10:30-11:00 (30m)
Break: Morning Break
15:00-15:30 (30m)
Break: Afternoon Break
12:30-13:30 (1h)
Break: Lunch