Computer Vision Course with Real-Life Cases: Boston, Venue is being confirmed. Stay tuned!, Monday, 18. November 2019

Truly Practical Computer Vision Course with Real-Life Cases

Why this training?
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.

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.

Course objectives

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.

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

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

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

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.

At the end of the course, all participants receive a certificate of attendance. This certificate includes the training duration and contents, and proves the attendee’s knowledge of the emerging technology.

Altoros recommends that all students have:
- 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 of matrix vector operations and notation
- Basic knowledge of statistics
- Basic knowledge of 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
All these libraries will be installed using Anaconda.
Requirements for the workstation:
- 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)
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.

Payment info:
If you would like to get an invoice for your company to pay for this training, please email to and provide us with the following info:

Name of your Company/Division which you would like to be invoiced;

Name of the person the invoice should be addressed to;

Mailing address;

Purchase order # to put on the invoice (if required by your company).

Please note our classes are contingent upon having 5 attendees. If we don't have enough tickets sold, we will cancel the training and refund your money one week prior to the for the understanding.

Computer Vision Course with Real-Life Cases: Boston

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