Szczecin

Biuro Aloha, Jerzego Janosika 17, Szczecin, Polska, 71-424

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Fiji: Introduction to Scientific Image Processing

21 godzin
Fiji 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
  • 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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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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PaddlePaddle

21 godzin
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu. In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications. By the end of this training, participants will be able to:
  • Set up and configure PaddlePaddle
  • Set up a Convolutional Neural Network (CNN) for image recognition and object detection
  • Set up a Recurrent Neural Network (RNN) for sentiment analysis
  • Set up deep learning on recommendation systems to help users find answers
  • Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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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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Scilab

14 godzin
Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems. In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing. By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge. Audience
  • Data scientists and engineers, especially with interest in image processing and facial recognition
Format of the course
  • Part lecture, part discussion, exercises and intensive hands-on practice, with a final project
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Opinie uczestników (14)

La quantité des exercices réalisés. L'aide du formateur sur chacun des problèmes rencontrés pendant les exercices. Il nous clarifie la démarche sans donner le résultat.

Fabien Follet - Autoliv
Szkolenie - Scilab

Les compétences du formateur, et la bonne ambiance.

Sébastien CADET - Autoliv
Szkolenie - Scilab

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

Przykłady z życia oraz praktyczne podejście do tematu.

Damian Pierończyk - Tech-Com sp. z o.o.
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

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

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