New York (NYC) - Midtown Manhattan - Park Ave.

E48-49th (Grand Central), 299 Park Avenue, New York City, united states, 10171

The venue is located in between the Waldorf Astoria and J.P. Morgan and Chase.

New York (NYC) - Midtown Manhattan - Madison Ave.

E38-39th, 260 Madison Ave., New York, united states, 10016
The venue is located just opposite of Madison Av & E 38 St bus stop and Zuma NYC, in the same building as Chase Bank.

Explore Our Courses

Computer Vision with OpenCV

 28 Hours
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 Hours
This instructor-led, live training in New York City (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.
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Raspberry Pi + OpenCV for Facial Recognition

 21 Hours
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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Testimonials (1)

I genuinely enjoyed the hands-on approach.

Kevin De Cuyper
Course - Computer Vision with OpenCV

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