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HDP Data Science: Deep Learning

Overview

This class is designed to cover key theory and background elements of deep learning, along with hands-on activities using both TensorFlow and Keras – two of the most popular frameworks for working with neural networks. In order to gain an intuitive understanding of deep learning approaches together with practice in building and training neural nets, this class alternates theory modules and hands-on labs.

Prerequisites

The class communicates the mathematical aspects of deep learning in a clear, straightforward way, and does not require a background in vector calculus, although some background in calculus, linear algebra, and statistics is helpful. All code examples and labs are done with Python, so previous experience with Python is recommended.

Target Audience

This class is ideal for engineers or data scientists who want to gain an understanding of neural net models and modern techniques, and start to apply them to real-world problems.

Live Training

Live Training Self Paced Blended
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