Course Outline

Introduction

TensorFlow Overview

  • What is TensorFlow?
  • TensorFlow features

What is AI

  • Computational Psychology
  • Computational Philosophy

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Deep Learning

  • Artificial neural networks
  • Deep learning vs. machine learning

Preparing the Development Environment

  • Installing and configuring TensorFlow

TensorFlow Quick Start

  • Working with nodes
  • Using the Keras API

Fraud Detection

  • Reading and writing to data
  • Preparing features
  • Labeling data
  • Normalizing data
  • Splitting data into test data and training data
  • Formatting input images

Predictions and Regressions

  • Loading a model
  • Visualizing predictions
  • Creating regressions

Classifications

  • Building and compiling a classifier model
  • Training and testing the model

Summary and Conclusion

Requirements

  • Python programming experience

Audience

  • Data Scientists
  14 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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