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Introduction to Application Development with TensorFlow and Keras Training

LEVEL: FOUNDATION

This 2-day, hands-on TensorFlow and Keras course covers the development of a real-world application powered by TensorFlow and Keras. You'll get a hands-on introduction to TensorFlow and Keras, including model architecture and evaluation. By the end of the course, you will deploy 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.

TensorFlow is a 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. If you are a developer, analyst, or data scientist interested in developing applications using TensorFlow and Keras, this course will give you the start you need.

Key Features of this Introduction to Application Development with TensorFlow and Keras Training:

  • After-course instructor coaching benefit

Who Should Attend 

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

What is TensorFlow?

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++.

What is Keras?

Keras is a leading High-level API. It is written in python and was created to be user friendly and modular.

What is the difference between Keras and TensorFlow?

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.

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Learning Objectives

  • Build an application that generates predictions using a deep learning model.
  • Improve the example model—either by adding more data, computing more features, or changing its architecture.
  • Continuously increase the model's prediction accuracy, or create a completely new model, changing the core components of the application as you see fit.

Course Content

Lesson 1: Introduction to Neural Networks and Deep Learning

  • What are Neural Networks?
  • Configuring a Deep Learning Environment

Lesson 2: Model Architecture

  • Choosing the Right Model Architecture
  • Using Keras as a TensorFlow Interface

Lesson 3: Model Evaluation

  • Model Evaluation
  • Hyperparameter Optimisation

Lesson 4: Productization

  • Handling New Data
  • Deploying a Model as a Web Application

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