Truly Practical Computer Vision Course with Real-Life Cases

Already familiar with classic machine learning and now ready to move to the next step? The Practical Computer Vision course will provide you with practical means of solving business-specific tasks. During this course, attendees will proceed from theory to expert-led hands-on practice that encompasses a set of real-life use cases. In addition, you can submit a use case of choice to develop the expertise needed for your current business concerns.

Why enroll

  • Find out how powerful some of the machine learning techniques might be;
  • Explore the mechanisms for learning neural networks means of neural network learning process management;
  • Learn how convolutional neural networks enhance machine learning for spatial data processing;
  • Get familiar with the architectures that solve basic computer vision tasks;
  • Get a sample code for training your own models to fit the needs of the business.
  • Each trainee will have 16 hours of online Computer Vision practice with a personal trainer on the project of your choice.

Who should attend

  • Everyone willing to improve knowledge and learn how to resolve issues challenging for classic machine learning;
  • Engineers who want to gain practical expertise in computer vision and apply it to real-life business tasks.

Training program

see details
Intro to Deep Learning
  • Explanation of a machine learning technique that are proven to be surprisingly powerful for a wide margin of tasks. We’ll look at a surprisingly strong machine learning techniques that have become really popular recently and will cover the following topics:
    • Structure of neural networks, feedforward neural networks
    • A mechanism for learning neural networks
    • Means of neural network learning process control
see details
Convolutional Neural Networks
  • Neural network architecture for image processing. Successes of convolutional neural networks was the reason of a new wave of interest in machine learning. Convolution as the core of the neural network layer for spatial data processing. Topics for the day:
    • Image features and representation learning
    • A convolution layer and a deep convolutional network
    • Supporting layers for convolutional neural networks
    • State-of-the-art architectures for image processing
    • Transfer learning and fine tuning
see details
Computer Vision
  • Computer vision drastically changed after the introduction of neural networks. In this module we'll try to cover a basic tasks of computer vision using neural networks. During the lectures we'll cover the architectures that solve the basic of the Computer Vision tasks and cover the following topics:
    • Image-specific data transformations
    • Architectures for Object Detection tasks
    • Architectures for Semantic Segmentation tasks
see details
16 hours of hands-on practice
  • Prediction on photo data set. We will learn how to build models for detecting objects on images. Using satellite images, we’ll create a model to detect: trace segmentation, roadmap mining, ship detection.
  • Prediction on video data set. In this task, video material will be used to build the model. Based on a set of video clips from fishing vessels, we’ll create a fish detection model.
  • Your own project. Each trainee can propose a project they'd like to work on.
See details

Altoros recommends that all students have:

  • Programming: Basic Python programming skills, a capability to work effectively with data structures
  • Experience with the Jupyter Notebook applications
  • Basic experience with Git
  • A basic understanding matrix vector operations and notation
  • A basic knowledge of statistics
  • Basic command line operations

All code will be written in Python with the use of the following libraries:

  • Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one
  • Scikit-learn
  • Matplotlib

A workstation with the following capabilities:

  • A web browser (Chrome/Firefox)
  • Internet connection
  • A firewall allowing outgoing connections on TCP ports 80 and 443

The following developer utilities should be installed:

  • Anaconda
  • Jupyter Notebook (will be installed using Anaconda)

All these libraries will be installed using Anaconda

If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

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Alexandra Mironova

Alexandra Mironova

Training Coordinator


location icon830 Stewart Dr., Suite 119Sunnyvale, CA 94085
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