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Course Outline

Introduction

TensorFlow Overview

  • What is TensorFlow?
  • Key features of TensorFlow

Understanding AI

  • Computational Psychology
  • Computational Philosophy

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Deep Learning

  • Artificial neural networks
  • Deep learning versus machine learning

Setting Up the Development Environment

  • Installing and configuring TensorFlow

TensorFlow Quick Start

  • Working with nodes
  • Utilising the Keras API

Fraud Detection

  • Reading and writing data
  • Feature preparation
  • Data labelling
  • Data normalisation
  • Splitting data into training and test sets
  • Formatting input images

Predictions and Regressions

  • Loading a model
  • Visualising predictions
  • Creating regressions

Classifications

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

Summary and Conclusion

Requirements

  • Experience with Python programming

Target Audience

  • Data Scientists
 14 Hours

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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