IA117
Artificial Intelligence

Practical application of convolutional neural networks (CNN)

Design, train, and deploy computer vision models


This intensive training course provides an understanding of and teaches how to implement convolutional neural networks (CNNs), which are widely used in image processing and computer vision.

Objectives

Learn how to design, train, and evaluate CNN models for practical applications such as image classification, object detection, and facial recognition.

Is it for you ?

AI Developers / Data Scientists, Computer Vision Engineers

Prerequisite

• Basic knowledge of Python • Understanding of machine learning concepts (regression, classification) • Understanding of matrices and linear algebra

Your benefits

  • Build and train CNN models for image classification using Keras
  • Prepare and enhance datasets with normalization, augmentation, and annotation
  • Optimize model performance using advanced architectures and hyperparameter tuning
  • Evaluate and compare models with appropriate metrics and validation techniques
  • Deploy computer vision solutions using transfer learning on real-world use cases
  • Content

    Fundamentals of CNNs

    Chapter 1: Introduction to convolutional neural networks

    • History and applications
    • Comparison with traditional networks (MLP)
    • Basic architecture: convolution, pooling, activation

    Chapter 2: Data preparation

    • Image formats and normalization
    • Data augmentation
    • Annotation and standard datasets (MNIST, CIFAR, ImageNet)

    Workshop 1: Building a first CNN with Keras

    • MNIST image classification
    • Visualization of layers and filters
    See more + / -

    CNN model design and optimization

    Chapter 3: Advanced architectures

    • VGG, ResNet, Inception
    • Deep networks and overfitting issues
    • Dropout, batch normalization

    Chapter 4: Training and evaluation

    • Cost function and metrics
    • Cross-validation
    • Early stopping and hyperparameter tuning

    Workshop 2: Implementing a CNN model on CIFAR-10

    • Comparing multiple architectures
    • Performance analysis

    Practical applications and transfer learning

    Chapter 5: Detection and recognition

    • Object detection (YOLO, SSD)
    • Facial recognition
    • Image segmentation

    Chapter 6: Transfer learning and fine-tuning

    • Using pre-trained models
    • Adapting to specific datasets
    • Advantages and limitations

    Workshop 3: Final project – Object detection in real images

    • Using pre-trained MobileNet or ResNet
    • Deploying a simple model

    💡 Useful information

    Our training sessions are offered in Montreal or Quebec City, in person or in virtual format. Dates and locations are provided when you select your session below. If you have any questions regarding registration, schedules, the language of instruction, or cancellation policies, please consult our FAQ .

    Duration
    3 days
    Schedule
    9h to 16h
    Regular fee
    $1,485
    Preferential fee A preferential rate is offered to public institutions, to members of certain professional organizations as well as to companies that do a certain amount of business with Technologia. To know more, please read the "Registration and rates" section on our FAQ page. Please note that preferential rates are not available for online training courses. Discounts cannot be combined with other offers.
    $1,335
    Private or personalized training

    Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.

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    Request in-company training

    Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.

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