Computer Vision with Python Training Course

Caffe is a deep learning framework designed with expression, speed, and modularity in mind.

This course delves into the application of Caffe as a deep learning framework for image recognition, using MNIST as an example.

Audience

This course is ideal for deep learning researchers and engineers who are interested in leveraging Caffe as their framework.

After completing this course, participants will be able to:

• understand the structure and deployment mechanisms of Caffe
• perform installation, production environment setup, architectural tasks, and configuration
• evaluate code quality, conduct debugging, and monitoring
• implement advanced production tasks such as training models, creating layers, and logging

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

Fiji is an open-source image processing package that combines ImageJ (an image processing program designed for scientific multidimensional images) with various plugins for advanced image analysis.

In this instructor-led, live training, participants will learn how to utilize the Fiji distribution and its underlying ImageJ program to develop an image analysis application.

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

• Utilize Fiji's advanced programming features and software components to extend the capabilities of ImageJ
• Stitch together large 3D images from overlapping tiles
• Automatically update their Fiji installation at startup using the integrated update system
• Choose from a wide range of scripting languages to create custom image analysis solutions
• Leverage Fiji's powerful libraries, such as ImgLib, for processing large bioimage datasets
• Deploy their application and collaborate with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Uzbekistan (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.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

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

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license, which contains several hundred algorithms for computer vision.

Audience

This course is designed for engineers and architects who are looking to leverage OpenCV for their computer vision projects.

This instructor-led, live training in Uzbekistan (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.

Pattern Matching is a technique employed to identify specific patterns within an image. It helps determine whether certain characteristics are present in a captured image, such as the correct label on a defective product on a factory line or the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on large collections of related samples, Pattern Matching specifies what we are looking for and confirms whether the expected pattern is present.

Format of the Course

• This course provides an introduction to the approaches, technologies, and algorithms used in the field of pattern matching as it pertains to Machine Vision.

SimpleCV is an open-source framework, which means it is a collection of libraries and software designed for developing vision applications. It enables you to work with images or video streams from webcams, Kinects, FireWire cameras, IP cameras, or mobile phones. This framework helps you create software that not only captures visual data but also interprets and understands the world around it.

Audience

This course is aimed at engineers and developers who are interested in building computer vision applications using SimpleCV.

This instructor-led, live training in Uzbekistan (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.