CA, Sacramento - Promenade Circle

180 Promenade Circle, Sacramento, united states, 95834
The venue is located just off the Dwight D. Eisenhower Highway, in the vicinity of The Promenade and Best Buy.

Subcategories (2)

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OpenCV
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Machine Vision

Explore Our Courses

YOLOv7: Real-time Object Detection with Computer Vision

 21 Hours
This instructor-led, live training in Sacramento (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.
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Computer Vision with SimpleCV

 14 Hours
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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Deep Learning for Vision with Caffe

 21 Hours
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 Hours
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 Hours
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 Hours
This instructor-led, live training in Sacramento (online or onsite) is aimed at developers who wish to build a self-driving car 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.
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Hardware-Accelerated Video Analytics

 14 Hours
This instructor-led, live training in Sacramento (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.
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Real-Time Object Detection with YOLO

 7 Hours
This instructor-led, live training in Sacramento (online or onsite) is aimed at back-end 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.
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Pattern Matching

 14 Hours
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 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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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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Python and Deep Learning with OpenCV 4

 14 Hours
This instructor-led, live training in Sacramento (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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Testimonials (2)

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
Course - Computer Vision with Python

I genuinely enjoyed the hands-on approach.

Kevin De Cuyper
Course - Computer Vision with OpenCV

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