In-Person Training
Deep Learning with TensorFlow

Season Yang (McKinsey & Company)
Tuesday, June 18 & Wednesday, June 19, 09:00 - 17:00
与人工智能交互 (Interacting with AI)
Location: 多功能厅5A+B(Function Room 5A+B)




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.

Day 1:

  • Introduction to Tensorflow
  • Iterative Algorithms
  • Machine Learning
  • Basic Neural Networks

Day 2:
  • Deep Neural Networks
  • Variational Autoencoders
  • Convolutional Neural Networks
  • Adversarial Noise
  • DeepDream
  • Recurrent Neural Networks


Photo of Season Yang

Season Yang is an analytics fellow in McKinsey & Company’s Risk Practice. Previously, Season was a data scientist in residence at the Data Incubator, where he also contributes to curriculum development and instruction and worked at NASA’s Goddard space center, where he studied climate change models with data analysis. Season holds a double Bachelor’s degree in applied mathematics and scientific computation and economics from UC Davis, and a Master’s in applied mathematics from Columbia, specializing in numerical computation.


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