Białystok

NobleProg Classroom, ul. Malmeda 1, Białystok, poland, 15-440

Na terenie Białegostoku, NobleProg ściśle współpracuje z firmą T-Matic, której sale szkoleniowe położone są w sa...

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OpenCV
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Machine Vision

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YOLOv7: Real-time Object Detection with Computer Vision

21 godzin
YOLOv7 is a state-of-the-art real-time object detection model for computer vision tasks.  This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level developers, researchers, and data scientists who wish to learn how to implement real-time object detection using YOLOv7. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of object detection.
  • Install and configure YOLOv7 for object detection tasks.
  • Train and test custom object detection models using YOLOv7.
  • Integrate YOLOv7 with other computer vision frameworks and tools.
  • Troubleshoot common issues related to YOLOv7 implementation.
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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Deep Learning for Vision with Caffe

21 godzin
Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to:
  • understand Caffe’s structure and deployment mechanisms
  • carry out installation / production environment / architecture tasks and configuration
  • assess code quality, perform debugging, monitoring
  • implement advanced production like training models, implementing layers and logging
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Marvin Framework for Image and Video Processing

14 godzin
Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java.  Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation. Some of Marvin's video applications include filtering, augmented reality, object tracking and motion detection. In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application. Format of the Course
  • The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.
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Computer Vision with Python

14 godzin
Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python. By the end of this training, participants will be able to:
  • Understand the basics of Computer Vision
  • Use Python to implement Computer Vision tasks
  • Build their own face, object, and motion detection systems
Audience
  • Python programmers interested in Computer Vision
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Deep Learning for Self Driving Cars

21 godzin
Deep learning is a subfield of machine learning. It uses methods based on learning data representations and structures such as neural networks. Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano. This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques. By the end of this training, participants will be able to:
  • Use Keras to build and train a convolutional neural network.
  • Use computer vision techniques to identify lanes in an autonomos driving project.
  • Train a deep learning model to differentiate traffic signs.
  • Simulate a fully autonomous car.
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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Computer Vision with SimpleCV

14 godzin
SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too. Audience This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
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Hardware-Accelerated Video Analytics

14 godzin
Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc. This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data. By the end of this training, participants will be able to:
  • Install and configure the necessary development environment, software and libraries to begin developing.
  • Build, train, and deploy deep learning models to analyze live video feeds.
  • Identify, track, segment and predict different objects within video frames.
  • Optimize object detection and tracking models.
  • Deploy an intelligent video analytics (IVA) application.
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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Real-Time Object Detection with YOLO

7 godzin
YOLO (You Only Look Once) is an algorithm turned into pre-trained models for object detection. It is tested by the Darknet neural network framework, making it ideal for developing computer vision features based on the COCO (Common Objects in Context) dataset. The latest variants of the YOLO framework, YOLOv3-v4, allows programs to efficiently execute object locating and classifying tasks while running in real-time. This instructor-led, live training (online or onsite) is aimed at backend developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection. By the end of this training, participants will be able to:
  • Install and configure the necessary tools and libraries required in object detection using YOLO.
  • Customize Python command-line applications that operate based on YOLO pre-trained models.
  • Implement the framework of pre-trained YOLO models for various computer vision projects.
  • Convert existing datasets for object detection into YOLO format.
  • Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
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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Pattern Matching

14 godzin
Pattern 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
  • This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
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Computer Vision with OpenCV

28 godzin
OpenCV (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
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Python and Deep Learning with OpenCV 4

14 godzin
OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition. This instructor-led, live training (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.
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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Raspberry Pi + OpenCV for Facial Recognition

21 godzin
This 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.
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Opinie uczestników (14)

Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.

Anthea King - WesCEF
Szkolenie - Computer Vision with Python

The hands-on approach

Kevin De Cuyper
Szkolenie - Computer Vision with OpenCV

Przykłady z życia oraz praktyczne podejście do tematu.

Damian Pierończyk - Tech-Com sp. z o.o.
Szkolenie - OptaPlanner in Practice

przykłady i ćwiczenia

Kamil
Szkolenie - Introduction to Data Science and AI using Python

Kontakt z prowadzącą - Anna była bardzo dobrą rozmówczynią i świetną prowadzącą - słuchała naszych pytań, opinii i przemyśleń i bardzo aktywnie odpowiadała na nasze potrzeby - czy to pokazując dodatkowe rzeczy, albo modyfikując przykłady tak, by odzwierciedlały w jakiś sposób problemy, z jakimi możemy się spotkać w naszej dziedzinie operacji.

Bartosz Kratochwil
Szkolenie - Introduction to Data Science and AI using Python

The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide. It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.

Terry Strachan - Exel Computer Systems plc
Szkolenie - OptaPlanner in Practice

The course is very interesting being the main focus nowdays

mohamed taher - FAB banak Egypt
Szkolenie - Introduction to Data Science and AI (using Python)

Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course

Mohamed Ghowaiba - FAB banak Egypt
Szkolenie - Introduction to Data Science and AI (using Python)

Helpful and good listener .. interactive

Ahmed El Kholy - FAB banak Egypt
Szkolenie - Introduction to Data Science and AI (using Python)

Wszystkie przedstawione informacje

Jose Victor - si
Szkolenie - Artificial Intelligence (AI) for Managers

Przetłumaczone przez sztuczną inteligencję

Podane ćwiczenia i przykłady.

Marcos - si
Szkolenie - Artificial Intelligence (AI) for Managers

Przetłumaczone przez sztuczną inteligencję

Tematy związane z Machine Lerning.

Víctor Edgar - si
Szkolenie - Artificial Intelligence (AI) for Managers

Przetłumaczone przez sztuczną inteligencję

Postawa nauczyciela

Ivonne Guadalupe Avendaño Hernandez - si
Szkolenie - Artificial Intelligence (AI) for Managers

Przetłumaczone przez sztuczną inteligencję

Nauczane koncepcje były jasne, praktyczne i bardzo pomocne w zrozumieniu, jak korzystać z tego tematu AI i ML.

Miguel - si
Szkolenie - Artificial Intelligence (AI) for Managers

Przetłumaczone przez sztuczną inteligencję

Pozostałe regiony w Polsce

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Konsultacje

Computer Vision Konsultacje
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