Deep Learning and Artificial Intelligence with TensorFlow

Deep Learning is an advancement in machine learning technology that uses neural networks for building prediction models. In this course, students use TensorFlow, an open-source and industry standard library for machine learning developed by Google Brain.

TensorFlow allows distribution of computation across CPUs and multiple GPUs on a single computer—an enhancement that enables development of high-performance prediction models.

Students will build prediction models of different complexities, from simple linear logistic regression to convolutional neural network (CNN) and recurrent neural network (RNN) with long Short-term memory (LSTM).

By the end of the class, students have a working deep learning environment and sample projects. The class prepares students to pursue a career in data sciences.

Topics Include:

  • Deep learning and TensorFlow
  • Multilayer perceptrons
  • Advanced multilayer perceptrons
  • Convolutional neural networks
  • Recurrent neural networks
  • RNN - prediction with multilayer perceptron
  • RNN - prediction with long short term memory networks

Skills Needed: Moderate level of computer programming ability in Python, comfortable with an editor, familiarity with basic command-line operations on a laptop, and basic understanding of Machine Learning models.

Note(s): Students are required to bring Laptops for classroom work. For best performance: students are asked to reinstall Python 3+ version of Anaconda distribution from their machines, if it is already loaded.


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