Programele de pregătire locală, instruite de un profesor grafic GPU (Unitatea de procesare grafică) demonstrează, prin discuții interactive și practici practice, fundamentele GPU-ului și modul de programare a unităților de procesare grafică. Pregătirea GPU este disponibilă ca "formare live la fața locului" sau "formare live la distanță". Training-ul live la fața locului poate fi efectuat la fața locului la sediul clientului Moldova sau în centrele de formare corporativa NobleProg din România Moldova . Instruirea live la distanță este realizată printr-un desktop interactiv, la distanță. NobleProg - Furnizorul dvs. de formare locală
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Mărturii
Exemple discutate în direct, digresiuni, exemple din "viață". O formă de formare, adică împletind o prelegere cu exemple practice și discutând aceste exemple.
Piotr Glazor - Nokia
Curs: NVIDIA GPU Programming - Extended
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toate
Dominik Kutten
Curs: Adobe LiveCycle Designer
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organizarea de cursuri
Jarosław Jasiński
Curs: Adobe Illustrator
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cunoștințele și reactivitatea față de probleme T
Tadeusz Kopryaniuk
Curs: Adobe Illustrator
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Instructorul ne-a țintit într-adevăr într-un studiu de caz foarte specific și a fost capabil să se adapteze situației (ca soluții la problemele noastre evoluate pe parcursul cursului), dincolo de pregătirea din amonte pe care a făcut-o.
Anne-Sophie Schwindenhammer
Curs: Inkscape
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totul.
Janusz Magnuszewski - mLeasing
Curs: Adobe LiveCycle Designer
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lucrați pe exemple
Kacper Gardziała - Exacto Sp. z o.o.
Curs: Adobe Illustrator
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Concentrându-se pe nevoile de instruit
Barbara Szubert-Tabaczka - Volkswagen Poznań Sp. z o.o.
Curs: Adobe LiveCycle Designer
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Cunoașterea și modul în care este comunicat.
Magdalena Rokoszewska - Agencja Kreatywna Przereklamowani
Curs: Techniki graficzne (Adobe Photoshop, Adobe Illustrator)
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Exerciţii
Intel Technology Poland SP. z o.o.
Curs: Inkscape
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Cunoștințele prezentatorului, subiectul, accesul la portal cu exerciții, este că cursul a fost înregistrat și pot să-l ascult într-un timp.
Intel Technology Poland SP. z o.o.
Curs: Inkscape
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GPU (Graphics Processing Unit) Subcategories
GPU (Graphics Processing Unit) Course Outlines
Numele cursului
Durata
Sinoptic
Numele cursului
Durata
Sinoptic
14 hours
Sinoptic
This course covers how to program GPUs for parallel computing. Some of the applications include deep learning, analytics, and engineering applications.
21 hours
Sinoptic
This instructor-led, live training course in Moldova covers how to program GPUs for parallel computing, how to use various platforms, how to work with the CUDA platform and its features, and how to perform various optimization techniques using CUDA. Some of the applications include deep learning, analytics, image processing and engineering applications.
14 hours
Sinoptic
This instructor-led, live training in Moldova (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.
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.
14 hours
Sinoptic
This instructor-led, live training in Moldova (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.
By the end of this training, participants will be able to:
- Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
- Create, compile and launch custom CUDA kernels.
- Manage GPU memory.
- Convert a CPU based application into a GPU-accelerated application.
By the end of this training, participants will be able to:
- Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
- Create, compile and launch custom CUDA kernels.
- Manage GPU memory.
- Convert a CPU based application into a GPU-accelerated application.
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