Generative Al Computer Vision Specialization | Level 1

Join Our Exclusive Event on Generative Al Computer Vision!

Embark on a captivating journey where Computer Vision meets Generative AI! Immerse yourself in an intensive learning experience that explores fundamental concepts and techniques.

Through meticulously designed modules, you'll explore key topics including Convolutional Neural Networks (CNN), image analysis algorithms, object detection, feature extraction, and much more. It then moves on to discuss image, and audio generation algorithms like GANs, Diffusion, VAEs and Transformers for creating an out-of-the world experience!

This event isn't just about theory – it's hands-on and practical. Engage in real-world case studies, interactive exercises, and applications that ensure you grasp the nuances of Generative AI Computer Vision. Whether you're a software engineer, Computer Vision specialist, AI enthusiast, or a data scientist, this event is your gateway to confidently applying Computer Vision methods across various domains.

Outcomes and Benefits of Attending:

• Master essential Computer Vision algorithms and techniques.
• Apply your knowledge to real-world scenarios through practical exercises.
• Enhance your skills in Generative AI models and their implementation.
• Network with fellow professionals and enthusiasts in the AI field.
• Elevate your capabilities in building intelligent solutions.

Prerequisite: Foundation for Generative AI course attendance or equivalent knowledge.

Don't miss this opportunity to elevate your expertise and explore the limitless possibilities of Computer Vision Algorithms. Secure your spot today and take a leap towards innovation!

Limited spots available to ensure an enriching learning experience.

Topics Covered

Module 1: Recap of key concepts of Computer Vision

• Fundamentals of Computer Vision - CNN Overview
• Edge Detection and Padding
• Strided Convolutions and Convolutions Over Volume
• One Layer of a Convolutional Network
• Building a Simple Convolutional Network
• Exploring Pooling Layers
• Deepening Understanding with a CNN

Module 2: Key Computer Vision Algorithms

• Overview of Popular image recognition algorithms:               
  • VGG
  • YOLOv3, YOLOv7
  • ResNet
  • AlexNet

      

• Understanding GAN
• Approaches for GANs               
  • CycleGAN
  • pix2pix

      

• Image Reconstruction               
  • Autoencoders

      

• Concepts related to Style Transfer

Module 3: GenerativeAI Overview and Use Cases

• The Power of Generative AI               
  • Impact on communication, work, and innovation
  • Case study: ChatGPT

      

• Text Generation               
  • Overview of text generation techniques: Markov Chains, RNNs, Transformers
  • Application: ChatGPT

      

• Image Generation               
  • Deep learning algorithms: VAEs, GANs, Stable Diffusion
  • Transformer Models in Image Generation.
  • Application: MidJourney, DALL-E

      

• Video and Speech Generation               
  • Video Generation techniques: GANs, Video Diffusion
  • Speech Generation using Transformers
  • Application: DeepBrain, Synthesia

      

HandsOn Lab Exercise - Training a Image Recognition Model

• Train YOLO Based Object Detection Model