Python and Deep Learning with OpenCV 4 - Plan Szkolenia

Kod kursu

opencv4deep

Czas trwania

14 godzin (zwykle 2 dni wliczając przerwy)

Wymagania

  • Basic programming experience

Audience

  • Software Engineers

Opis

OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition.

This instructor-led, live training (onsite or remote) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

By the end of this training, participants will be able to:

  • View, load, and classify images and videos using OpenCV 4.
  • Implement deep learning in OpenCV 4 with TensorFlow and Keras.
  • Run deep learning models and generate impactful reports from images and videos.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Plan Szkolenia

Introduction

What is AI

  • Computational Psychology
  • Computational Philosophy

Deep Learning

  • Artificial neural networks
  • Deep learning vs. machine learning

Preparing the Development Environment

  • Installing and configuring OpenCV

OpenCV 4 Quickstart

  • Viewing images
  • Using color channels
  • Viewing videos

Deep Learning Computer Vision

  • Using the DNN module
  • Working with with deep learning models
  • Using SSDs

Neural Networks

  • Using different training methods
  • Measuring performance

Convolutional Neural Networks

  • Training and designing CNNs
  • Building a CNN in Keras
  • Importing data
  • Saving, loading, and displaying a model

Classifiers

  • Building and training a classifier
  • Splitting data
  • Boosting accuracy of results and values

Summary and Conclusion

Opinie uczestników

★★★★★
★★★★★

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