Course

Deep Learning and Artificial Intelligence with TensorFlow and Keras | DBDA.X425

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.

Learning Outcomes:
At the conclusion of the course, you should be able to:

Use the common deep learning architectures such as CNN and RNN that are used in the industry
Discuss the significance of hyper-parameters in the architectures
Prepare data for deep learning using Pandas and NumPy, the de-facto standard for data prep in Python
Write scalable TensorFlow and Keras code that can be used to train deep learning architectures on real-world business problems
Debug and understand the inner working of deep learning architectures

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

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

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.

Have a question about this course?