Beginning Application Development with TensorFlow and Keras

Beginning Application Development with TensorFlow and Keras

Course Length: 2 days
Delivery Methods: Available as private class only
Course Overview

This Beginning Application Development with TensorFlow and Keras training class covers the development of a real-world application powered by TensorFlow and Keras. TensorFlow is popular software created by Google (and open source contributors) to facilitate the development of machine learning applications, particularly those that use deep learning. Keras is an interface that facilitates the development of deep learning models.

The course starts with a hands-on introduction to TensorFlow and Keras. Then we move to the architecture of an example model, selecting the right layers to solve an example problem (predicting Bitcoin prices). Then we move on to the training and evaluation of the model. We will finish by deploying the model as a real-world product: a web-application (with an HTTP API) that uses Flask to make our model predictions available to the world.

This is a 2-day course packaged with the right balance of theory and hands-on activities that will help you easily learn TensorFlow and Keras from scratch.

This course is designed for developers, analysts, and data scientists interested in developing applications using TensorFlow and Keras.

Course Benefits
  • Obtain a blueprint of the complete process for deploying a deep learning application: from environment setup to model deployment.
  • Obtain a hands-on introduction to TensorFlow and Keras, popular technologies for building production-grade deep learning models.
  • Get started with an example web application that uses an HTTP API interface to retrieve model predictions.
Course Outline
  1. Introduction to Neural Networks and Deep Learning
    1. What Are Neural Networks?
    2. Configuring a Deep Learning Environment
  2. Model Architecture
    1. Choosing the Right Model Architecture
    2. Using Keras as a TensorFlow Interface
  3. Model Evaluation and Evaluation
    1. Model Evaluation
    2. Hyperparameter Optimization
  4. Productization
    1. Handling New Data
    2. Deploying a Model as a Web Application
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

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  • Expert Instructors
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