Machine Learning – Modern Computer Vision & Generative AI: Use KerasCV, Python, Tensorflow, JAX for Image Recognition, Object Detection, and Stable Diffusion

Lazy Programmer

€82.99

Video Feb 2024 6hrs 4mins 1st Edition

Lazy Programmer

€82.99

Video Feb 2024 6hrs 4mins 1st Edition

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Key benefits

Comprehensive coverage ranging from basic image classification to advanced generative AI techniques
Focuses on real-world uses of AI in image analysis and content generation
Hands-on experience with Stable Diffusion for generative art

Description

This is an innovative course tailored for those who aspire to master the dynamic fields of computer vision and generative artificial intelligence. This course offers a deep dive into the world of image recognition, object detection, and the magic of generative AI using tools like KerasCV, Python, TensorFlow, PyTorch, and JAX. This course guides you from the fundamentals of image classification to the intricacies of object detection, starting with the KerasCV library for deep learning. You'll learn to effectively use and fine-tune pre-trained models, create custom datasets for object detection, using tools like the LabelImg GUI program, and apply these skills to tackle real-world challenges. A key focus is the generative AI segment, particularly on Stable Diffusion, where you'll learn to generate detailed images from text, unlocking your creative potential in this advanced AI domain. This course offers a progressive learning path from basic concepts to advanced techniques, catering to both professional development and creative pursuits in AI. By the end, you'll have the confidence and tools needed for a variety of applications in the ever-evolving field of generative artificial intelligence.

Who is this book for?

This course is ideal for individuals ranging from beginners to advanced learners who possess a fundamental understanding of machine learning and Python programming. It is especially beneficial for those keen on delving into the realm of computer vision using KerasCV.