Application Development with TensorFlow & Keras

PYTH-150 (2 Days)

TensorFlow & Keras Training Overview

Accelebrate's Application Development with TensorFlow & Keras training teaches attendees how to develop real-world machine learning applications powered by TensorFlow and Keras, popular technologies for building production-grade deep learning models. Students also learn how to use an HTTP API to retrieve model predictions.

Location and Pricing

Accelebrate courses are taught as private, customized training for groups of 3 or more at your site. In addition, we offer live, private online training for teams who may be in multiple locations or wish to save on travel costs. To receive a customized proposal and price quote for private on-site or online training, please contact us.

TensorFlow & Keras Training Objectives

All students will:

Understand the necessary technical requirements for developing a TensorFlow-powered application
Gain knowledge on different deep learning architectures and start to develop an intuition on what to chose for your problem
Experiment with different deep learning algorithms
Train and evaluate the performance of deep learning models
Combine your model with Flask to create an HTTP API that returns model predictions

TensorFlow & Keras Training Outline

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Introduction

Overview neural networks and deep learning
Organizing a project directory
Setting-up programming environment
- Installing Python 3
- Creating a Python virtual environment
- Installing application dependencies
- Configuring TensorFlow, Keras, and TensorBoard

Model Architecture

Load and prepare Bitcoin dataset
- Load CSV file into memory
- Extracting and exploring dataset features
- Split dataset into a train and a test set
Overview of a TensorFlow computation graph (i.e. a "model")
Overview of the Keras interface
- Instantiating a model
- Adding components
- Creating a queue and parallelize computations
- Using the debugger
Deploying TensorBoard

Model Training and Evaluation

cl Training a model
- Epochs
- Weights and Biases
- Hidden layers
- Learning rate and optimization (i.e. activation functions)
Testing a model
- Measuring accuracy and error rates
Determining when to stop
Model deployment

Productization

Overview of an (extremely) simple Flask application
Creating an HTTP endpoint
Retrieving JSON results

Conclusion

50%

Lecture/Demo

50%

Lab

Course Number:

PYTH-150

Duration:

2 Days

Prerequisites:

All students should have programming experience, familiarity with Python 3, and basic knowledge of web-applications (i.e., how the HTTP protocol works). Knowledge of the following topics are useful, but not required:

Machine learning concepts such as the bias and variance tradeoff, and accuracy estimation methods
Familiarity with other machine learning algorithms such as Support Vector Machines, Adaptive Boosting (a.k.a. "AdaBoost"), Naïve Bayes, and Logistic Regression
Knowledge of statistics, probability, linear algebra, and calculus
Experience with data-provisioning systems, including file systems (local and remote) and databases (SQL and NoSQL)

Training Materials:

All TensorFlow students receive comprehensive courseware covering all topics in the class.

Software Requirements:

Windows, MacOS, or Linux
Anaconda or comparable Python distribution with TensorFlow and Keras installed
PyCharm, Spyder, or comparable Python IDE
Related lab files that Accelebrate provides
Internet access for all attendees and the instructor

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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877 849 1850

International:
+1 678 648 3113

Fax: +1 404 420 2491

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