In this course, we first start with an introduction to Deep Learning. Then we will look at the TensorFlow framework and preview its main components as well as the overall API hierarchy. TensorFlow 2.x was launched with tight integration of Keras, eager execution by default, and Pythonic function execution, among other new features and improvements.
Next, we will discuss how to train on large datasets using the Dataset API; how to use feature columns to prepare the data for training; and how activation functions are needed in order for the model to be able to learn nonlinearities in the data.
We introduce the tf.keras API which is TensorFlow's high-level API for building and training deep learning models. We will explore the Sequential and Functional APIs and learn how to use them to create deep learning models.
Finally, we discuss how to deploy models and use them to solve a real world problem.
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Feb 14 - 15
9:00 AM - 4:30 PM EST
Apr 4 - 5
9:00 AM - 4:30 PM EDT
Herndon, VA / Online (AnyWare)
Aug 15 - 16
9:00 AM - 4:30 PM EDT
Ottawa / Online (AnyWare)
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This course provides the skills and knowledge required to understand TensorFlow, and to use it to solve actual real-world complex problems.
Hands-On Experience Includes:
Every attendee has remote access to dedicated VM with TensorFlow and exercises installed for 3 months after the course to try out and practice
Anyone seeking to understand and exploit the benefits of TensorFlow to implement Machine Learning/AI: Data Scientists, developers and analysts, and anyone with some machine learning background.
TensorFlow is an open source library for numerical computation and it is used for large-scale machine learning. It uses Python as a front-end API for building applications with the framework, while executing those applications in high-performance C++.
Keras is a leading High-level API. It is written in python and was created to be user friendly and modular.
Keras is a high level library that cannot live on it's own, while TensorFlow is a framework that can live on it's own.