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 also allows for the evaluation of participants.
- 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.
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
Open Training Courses require 5+ participants.
Computer Vision with Google Colab and TensorFlow Training Course - Booking
Computer Vision with Google Colab and TensorFlow Training Course - Enquiry
Computer Vision with Google Colab and TensorFlow - Consultancy Enquiry
Consultancy Enquiry
Testimonials (1)
I genuinely enjoyed the hands-on approach.
Kevin De Cuyper
Course - Computer Vision with OpenCV
Upcoming Courses
Related Courses
Introduction to Google Colab for Data Science
14 HoursThis instructor-led, live training in France (online or onsite) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.
By the end of this training, participants will be able to:
- Set up and navigate Google Colab.
- Write and execute basic Python code.
- Import and handle datasets.
- Create visualizations using Python libraries.
Data Visualization with Google Colab
14 HoursThis instructor-led, live training in France (online or onsite) is aimed at beginner-level data scientists who wish to learn how to create meaningful and visually appealing data visualizations.
By the end of this training, participants will be able to:
- Set up and navigate Google Colab for data visualization.
- Create various types of plots using Matplotlib.
- Utilize Seaborn for advanced visualization techniques.
- Customize plots for better presentation and clarity.
- Interpret and present data effectively using visual tools.
AI Facial Recognition Development for Law Enforcement
21 HoursThis instructor-led, live training in France (online or onsite) is aimed at beginner-level law enforcement personnel who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.
By the end of this training, participants will be able to:
- Understand the fundamentals of Artificial Intelligence and Machine Learning.
- Learn the basics of digital image processing and its application in facial recognition.
- Develop skills in using AI tools and frameworks to create facial recognition models.
- Gain hands-on experience in creating, training, and testing facial recognition systems.
- Understand ethical considerations and best practices in the use of facial recognition technology.
Fiji: Introduction to Scientific Image Processing
21 HoursFiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis.
In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application.
By the end of this training, participants will be able to:
- Use Fiji's advanced programming features and software components to extend ImageJ
- Stitch large 3d images from overlapping tiles
- Automatically update a Fiji installation on startup using the integrated update system
- Select from a broad selection of scripting languages to build custom image analysis solutions
- Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets
- Deploy their application and collaborate with other scientists on similar projects
Format of the Course also allows for the evaluation of participants.
- 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.
Fiji: Image Processing for Biotechnology and Toxicology
14 HoursThis instructor-led, live training in France (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.
By the end of this training, participants will be able to:
- Navigate the Fiji interface and utilize ImageJ’s core functions.
- Preprocess and enhance scientific images for better analysis.
- Analyze images quantitatively, including cell counting and area measurement.
- Automate repetitive tasks using macros and plugins.
- Customize workflows for specific image analysis needs in biological research.
Machine Learning with Google Colab
14 HoursThis instructor-led, live training in France (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.
By the end of this training, participants will be able to:
- Set up and navigate Google Colab for machine learning projects.
- Understand and apply various machine learning algorithms.
- Use libraries like Scikit-learn to analyze and predict data.
- Implement supervised and unsupervised learning models.
- Optimize and evaluate machine learning models effectively.
Computer Vision with OpenCV
28 HoursOpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
Python and Deep Learning with OpenCV 4
14 HoursThis instructor-led, live training in France (online or onsite) 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.
OpenFace: Creating Facial Recognition Systems
14 HoursOpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Pattern Matching
14 HoursPattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course also allows for the evaluation of participants.
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Python Programming Fundamentals using Google Colab
14 HoursThis instructor-led, live training in France (online or onsite) is aimed at beginner-level developers and data analysts who wish to learn Python programming from scratch using Google Colab.
By the end of this training, participants will be able to:
- Understand the basics of Python programming language.
- Implement Python code in Google Colab environment.
- Utilize control structures to manage the flow of a Python program.
- Create functions to organize and reuse code effectively.
- Explore and use basic libraries for Python programming.
Raspberry Pi + OpenCV for Facial Recognition
21 HoursThis instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
- Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
- Configure OpenCV to capture and detect facial images.
- Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
- Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
Vision Builder for Automated Inspection
35 HoursThis instructor-led, live training in France (online or onsite) is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.
By the end of this training, participants will be able to:
- Set up and configure automated inspections using Vision Builder AI.
- Acquire and preprocess high-quality images for analysis.
- Implement logic-based decisions for defect detection and process validation.
- Generate inspection reports and optimize system performance.