Seats left: 10

Computer Vision with IBM

Image recognition and understanding is #1 topic in AI, from detecting a specific type of plants, counting chickens, automatic processing of car damage claims in insurance or for self-driving cars

Date: February 4 from 9:00 to NaN:aN Central European Time

The workshop includes lectures, discussions on how Watson image understanding tools can help you to achieve your goal, and hands-on exercises that participants can use to better understand what component to use and what for. During this workshop we'll look at a few examples and talk about the algorithms that are under the hood of ANN (artificial neural network) for image understanding. Then it'll be your time to get your hands-on one of this various tools and create your own solution.


Highlights: During this workshop, we will cover:

  • Visual Recognition Challenges & Use Cases
  • Visual Recognition Out-of-the-box tooling & custom models
  • APIs, CoreML on Apple device, Google ML Kit
  • NEuNetS (Neural network synthesis) autoML Watson Openscale tooling
  • Creating custom Neural network models with Watson Studio Jupiter notebook (Keras)
  • PowerAI Vision Solution overview
  • Existing usages and extended considerations



  • IBM Cloud and Watson AI introduction
  • Computer vision
    Challenges and Use Case Watson Visual Recognition Out-of-the-box tooling, Custom models creation CoreML and APIs
  • Lab 1: Using Visual Recognition defaults models with out-of-the-box tool
  • Building Custom Models
    AutoML considerations
  • NeuNetS overview & training
  • Lab 2: Creating a custom classifier with out-of-the-box NeuNetS tool
  • Building Custom Models from scratch
    Considerations for Image understanding (CNN)
  • Jupiter notebook with Keras ML framework
  • Deployment on WML
  • Lab 3: Creating a Keras CNN model for Handwritten digits
  • Introduction to out of the box PowerAI Vision Solution
    Watson Machine Learning stack overview
  • Demo of PowerAI Vision
  • Lab 4: Custom training & hands-on
  • Extended consideration





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