Deep Learning and Artificial Intelligence with TensorFlow and Keras

Deep learning is a branch of Artificial Intelligence and Machine Learning that uses multi-layered neural networks to create highly accurate prediction models for tasks such as image recognition, object detection, language translation, speech recognition, and others. In this course, students will use open-source and industry-standard machine learning libraries, TensorFlow and Keras to build and deploy deep learning models.

Students will build deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs). By the end of the class, students will be proficient in best practices of using TensorFlow and Keras.

The class prepares students to pursue a career in data sciences and AI model development.

Topics Include:

  • Deep learning and TensorFlow/Keras
  • Multilayer perceptrons
  • Advanced multilayer perceptrons
  • Convolutional neural networks
  • Image processing CNN architectures
  • 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 command-line operations on a laptop, and a basic understanding of Machine Learning models.

    Note(s): Students are required to bring laptops for the classroom and work with Python3/ Jupyter Notebook environment.


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