Deep Learning for Vision with Caffe Training Course

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in the UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in the UAE (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 is an open-source image processing package that integrates ImageJ (a program designed for scientific multidimensional images) along with numerous plugins for advanced scientific image analysis.

During this instructor-led live session, participants will learn how to utilize the Fiji distribution and its core ImageJ software to develop an application for image analysis.

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

• Leverage Fiji's sophisticated programming capabilities and software modules to enhance ImageJ
• Combine large 3D images from overlapping segments
• Automatically update a Fiji installation upon startup using its built-in update system
• Select from a wide range of scripting languages to create custom image analysis solutions
• Utilize powerful libraries like ImgLib for extensive bioimage datasets with Fiji
• Deploy their application and collaborate with other researchers on similar projects

Course Format

• Interactive lecture and discussion.
• Absorbing exercises and ample practice opportunities.
• Practical implementation in a live-lab setting.

Customization Options for the Course

• To request tailored training for this course, please contact us to arrange.

This instructor-led, live training in the UAE (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.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under BSD that encompasses hundreds of computer vision algorithms.

Audience

This course is designed for engineers and architects aiming to leverage OpenCV in their computer vision projects.

This instructor-led, live training in the UAE (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 is an open-source facial recognition software that operates in real-time and is built using Python and Torch, based on Google's FaceNet research.

In this instructor-led live training session, participants will learn how to utilize OpenFace’s components to develop and deploy a sample facial recognition application.

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

• Work with OpenFace’s components such as dlib, OpenCV, Torch, and nn4 to implement face detection, alignment, and transformation
• Apply OpenFace in practical applications like surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, among others.

Audience

• Developers
• Data scientists

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Pattern Matching is a method employed to identify specific patterns within an image. It helps ascertain whether certain features are present in a captured image, such as identifying the expected label on a faulty product on an assembly line or verifying the precise dimensions of a part. This technique differs from "Pattern Recognition," which identifies general patterns based on extensive collections of related samples, by specifically defining what needs to be found and then indicating if the anticipated pattern is present.

Course Format

• This course covers the methodologies, technologies, and algorithms utilized in the realm of pattern matching as it pertains to Machine Vision.

This instructor-led live training course introduces the necessary software, hardware, and step-by-step procedures for constructing a facial recognition system from the ground up. Facial Recognition is alternatively referred to as Face Recognition.

The lab equipment includes a Raspberry Pi, camera module, servos (optional), among others. Participants must procure these components independently. The software utilized encompasses OpenCV, Linux, Python, and other tools.

Upon completion of this training, participants will be able to:

• Install Linux, OpenCV, and additional software utilities and libraries on a Raspberry Pi.
• Configure OpenCV for capturing and detecting facial images.
• Explore various packaging options for deploying a Raspberry Pi system in real-world settings.
• Tailor the system to accommodate diverse use cases such as surveillance and identity verification.

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Note

• Alternative hardware and software options include Arduino, OpenFace, Windows, etc. If you wish to use any of these alternatives, please contact us to make arrangements.

Scilab is a robust, free, and open-source high-level programming language designed for handling scientific data. It is utilized in various fields such as statistics, graphics and animation, simulation, signal processing, physics, optimization, and more. The core data structure of Scilab is the matrix, which simplifies many types of problems compared to alternatives like FORTRAN and C derivatives. Additionally, it is compatible with languages such as C, Java, and Python, making it a valuable addition to existing systems.

In this instructor-led training session, participants will learn about the benefits of Scilab over alternatives like Matlab, understand the basics of Scilab syntax, explore some advanced functions, and interface with other commonly used programming languages based on demand. The course will culminate in a short project centered around image processing.

By the end of this training, participants will have a solid understanding of both basic and advanced functions of Scilab, along with resources to further expand their knowledge.

Audience

• Data scientists and engineers, particularly those interested in image processing and facial recognition

Format of the course

• The training will consist of lectures, discussions, exercises, intensive hands-on practice, and a final project.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This instructor-led, live training in the UAE (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.