Computer Vision with Google Colab and TensorFlow Training Course

Computer vision is a rapidly evolving field within artificial intelligence, and TensorFlow is one of the most powerful tools available for building and deploying vision models. This course introduces participants to advanced computer vision techniques using TensorFlow and Google Colab, covering essential areas such as convolutional neural networks (CNNs) and image processing techniques.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

Build and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for scalable and efficient cloud-based model development.
Implement image preprocessing techniques for computer vision tasks.
Deploy computer vision models for real-world applications.
Use transfer learning to enhance the performance of CNN models.
Visualize and interpret the results of image classification models.

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.

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Course Outline

Introduction to Computer Vision

Overview of computer vision applications
Understanding image data and formats
Challenges in computer vision tasks

Introduction to Convolutional Neural Networks (CNNs)

What are CNNs?
Architecture of CNNs: Convolutional layers, pooling, and fully connected layers
How CNNs are used in computer vision

Hands-On with TensorFlow and Google Colab

Setting up the environment in Google Colab
Using TensorFlow for model building
Building a simple CNN model in TensorFlow

Advanced CNN Techniques

Transfer learning for CNNs
Fine-tuning pre-trained models
Data augmentation techniques for improved performance

Image Preprocessing and Augmentation

Image preprocessing techniques (scaling, normalization, etc.)
Augmenting image data for better model training
Using TensorFlow’s image data pipeline

Building and Deploying Computer Vision Models

Training CNNs for image classification
Evaluating and validating model performance
Deploying models to production environments

Real-World Applications of Computer Vision

Computer vision in healthcare, retail, and security
AI-powered object detection and recognition
Using CNNs for face and gesture recognition

Summary and Next Steps

Requirements

Experience with Python programming
Understanding of deep learning concepts
Basic knowledge of convolutional neural networks (CNNs)

Audience

Data scientists
AI practitioners

21 Hours

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Testimonials (1)

I genuinely enjoyed the hands-on approach.