Machine Learning with TensorFlow & Keras


Course Number: PYTH-150

Duration: 2 days (13 hours)

Format: Live, hands-on

TensorFlow and Keras Training Overview

Accelebrate's  Machine Learning 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 offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

  • 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

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)

Outline

Expand All | Collapse All

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

Training Materials:

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

Software Requirements:

  • Windows, Mac, or Linux
  • Anaconda or comparable Python distribution with TensorFlow and Keras installed (Accelebrate will provide instructions)
  • PyCharm, Spyder, or comparable Python IDE
  • Related lab files that Accelebrate will provide


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