Get in Touch
 Duration 21 hours

Course Outline

Fundamentals of Computer Vision

  • Overview of computer vision use cases
  • Comprehending image data structures and formats
  • Key challenges in computer vision tasks

Foundations of Convolutional Neural Networks (CNNs)

  • Definition and role of CNNs
  • CNN architecture: Exploring convolutional layers, pooling, and fully connected layers
  • Application of CNNs within computer vision systems

Practical Application of TensorFlow and Google Colab

  • Configuring the workspace in Google Colab
  • Applying TensorFlow for model construction
  • Creating a basic CNN model in TensorFlow

Advanced Strategies for CNNs

  • Transfer learning applied to CNNs
  • Adjusting pre-trained models through fine-tuning
  • Employing data augmentation to optimize performance

Image Preprocessing and Data Augmentation

  • Standard image preprocessing methods (scaling, normalization, and more)
  • Enhancing image datasets for more robust model training
  • Utilizing TensorFlow’s image data pipeline capabilities

Development and Deployment of Vision Models

  • Training CNNs specifically for image classification
  • Assessing and validating model accuracy and performance
  • Implementing models in production environments

Practical Computer Vision Applications

  • Computer vision integration in healthcare, retail, and security sectors
  • AI-driven object detection and recognition systems
  • Applying CNNs for facial and gesture recognition

Requirements

  • Proficiency in Python programming
  • Familiarity with deep learning principles
  • Foundational knowledge of convolutional neural networks (CNNs)

Target Audience

  • Data scientists
  • AI professionals and practitioners

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

Related Categories