Deep Learning – Computer Vision Training

Catalogue index: IFS/17

  • In stock
  • Delivery: free
  • Dedicated training:
  • Producer:
  • Training level:
    • Zaawansowany
  • Type of training:
    • stacjonarnie; online
PLN2,326.67 net / particip. PLN34,900.05  net for 1 workshop PLN2,861.80 gross incl. 23% VAT / particip.

product- line 1

Features

Features
Certification:
  • TAK
Dedicated training:
Number of training hours:
  • 40
Producer:
Training language:
  • polski
Training level:
  • Zaawansowany
Type of training:
  • stacjonarnie; online

Description

Company Description

Infoshare is the largest tech community in CEE and the organizer of the leading tech conference in Gdańsk. It connects startups, investors, corporations, and innovation enthusiasts. It promotes entrepreneurship, knowledge sharing, and networking. Through events, competitions, and programs, it supports the development of the tech ecosystem in Poland and the region.

Training Description

Computer Vision (CV) is a field of science that defines how machines interpret the meaning of images and videos. Computer Vision algorithms analyze specific criteria in images and videos, and then apply interpretations to predictive or decision-making tasks.

Who the Training is For
  • For designers, creatives, and anyone working with images and videos, for whom machine learning significantly eases their work.
  • For those who would like to deepen their knowledge of more advanced topics related to images and videos.
Goals
Benefits
  • How to prepare image data for machine learning using OpenCV and preprocessing techniques
  • How to design and train convolutional neural networks (CNN) for image classification in TensorFlow/Keras
  • How to build and evaluate object detection models, including SSD detectors
  • How to improve model quality through tuning, transfer learning, and data augmentation
Training Program

  1. INTRODUCTION
     • What is an image
     • OpenCV
     • Classical image processing

  2. THEORY OF CONVOLUTIONAL NEURAL NETWORKS
     • Data set processing
     • Loading and reviewing images
     • Building a data set
     • Preprocessing
     • Preparation for modeling

  3. IMAGE CLASSIFICATION
     • Convolutional layers
     • Designing CNN in tf.keras
     • Modeling
     • Qualitative analysis of the model
     • Tuning the model

  4. OBJECT DETECTION
     • Types of detectors
     • Designing an SSD detector
     • Modeling the detector
     • Quality analysis

  5. TRANSFER LEARNING
     • Comparing models

  6. OBJECT DETECTION – CONTINUATION
     • Expansion
     • Low-level construction of CNN
     • Parameter analysis of the model
     • Data augmentation

Duration

40 h/ 5 days

Price includes

Zamów szkolenie