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Generative AI Computer Vision Specialization | Level1

Building Intelligent Computer Vision Solutions using Power of Generative AI


7.5 Hours


Intermediate Level

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This course immerses you in the captivating realm of Computer Vision Algorithms, offering participants a thorough exploration of fundamental concepts and techniques. Through a carefully crafted series of modules, students will dive into essential topics such as CNN,  image analysis algorithms, object detection, feature extraction, and more. The course covers practical applications, hands-on exercises, and real-world case studies to ensure a well-rounded understanding of Computer Vision. By course completion, participants will have gained the skills and knowledge needed to confidently apply Computer Vision techniques in a variety of contexts.

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  • Developers and software engineers interested in learning GenerativeAI
  • Computer Vision engineers looking to implement GenerativeAI models in various industry use cases.
  • AI enthusiasts and professionals aiming to build intelligent and innovative solutions
  • Even Data scientists and machine learning practitioners seeking to enhance their skills in working with GenAI models
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  • 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
  • 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
  • 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

Train YOLO Based Object Detection Model

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Participants should have attended Foundation for Generative AI course or have an equivalent knowledge.


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