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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