Course description

In this course students are introduced to the architecture of deep neural networks algorithms that are developed to extract high-level feature representations of data. In addition to theoretical foundations of neural networks including backpropagation and stochastic gradient descent students get hands-on experience building deep neural network models with Python. Topics covered in the course include image classification time series forecasting text vectorization (tf-idf and word2vec) natural language translation speech recognition and deep reinforcement learning. Students learn how to use application program interfaces (APIs) such as TensorFlow and Keras for building a variety of deep neural networks convolutional neural network (CNN) recurrent neural network (RNN) self-organizing maps (SOM) generative adversarial network (GANs) and long short-term memory (LSTM). Some of the models will require the use of graphics processing unit (GPU) enabled Amazon Machine Images (AMI) in Amazon Web Services (AWS) Cloud.

Instructor

  • Senior Research Analyst, Faculty of Arts and Sciences Office for Faculty Affairs, Harvard University

Associated Schools

  • Harvard Division of Continuing Education

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