Machine Learning: Modern Computer Vision & Generative AI

Overview

Use KerasCV, Python, Tensorflow, PyTorch, & JAX for Image Recognition, Object Detection, and Stable Diffusion What you'll learn:

Computer vision with KerasCVHow to do image classification / image recognition with a pretrained model and fine-tuning / transfer learningHow to do object detection with a pretrained model and fine-tuning / transfer learningHow to generate images with Stable Diffusion in KerasCV Welcome to "Machine Learning:

Modern Computer Vision & Generative AI," a cutting-edge course that explores the exciting realms of computer vision and generative artificial intelligence using the KerasCV library in Python. This course is designed for aspiring machine learning practitioners who wish to explore the fusion of image analysis and generative modeling in a streamlined and efficient manner.Course Highlights:

KerasCV Library:

We start by harnessing the power of the KerasCV library, which seamlessly integrates with popular deep learning backends like Tensorflow, PyTorch, and JAX.

KerasCV simplifies the process of writing deep learning code, making it accessible and user-friendly.Image Classification:

Gain proficiency in image classification techniques. Learn how to leverage pre-trained models with just one line of code, and discover the art of fine-tuning these models to suit your specific datasets and applications.Object Detection:

Dive into the fascinating world of object detection.

Master the art of using pre-trained models for object detection tasks with minimal effort. Moreover, explore the process of fine-tuning these models and learn how to create custom object detection datasets using the LabelImg GUI program.Generative AI with Stable Diffusion:

Unleash the creative potential of generative artificial intelligence with Stable Diffusion, a powerful text-to-image model developed by Stability AI.

Explore its capabilities in generating images from textual prompts and understand the advantages of KerasCV's implementation, such as XLA compilation and mixed precision support, which push the boundaries of generation speed and quality.Course Objectives:

Develop a strong foundation in modern computer vision techniques, including image classification and object detection.Acquire hands-on experience in using pre-trained models and fine-tuning them for specific tasks.Learn to create custom object detection datasets to tackle real-world problems effectively.Unlock the world of generative AI with Stable Diffusion, enabling you to generate images from text with state-of-the-art speed and precision.Enhance your machine learning skills and add valuable tools to your toolkit for various applications, from computer vision projects to generative art and content generation.Join us on this captivating journey into the realms of modern computer vision and generative AI. Whether you're a seasoned machine learning practitioner or just starting, this course will equip you with the knowledge and skills to tackle complex image analysis and creative AI projects with confidence.

Explore the cutting-edge possibilities that KerasCV and Stable Diffusion offer, and bring your AI aspirations to life.Prerequisites:

Basic knowledge of machine learning and Python programming. Familiarity with deep learning concepts is beneficial but not mandatory.

Syllabus

• Introduction to Modern Computer Vision & Generative AI
Overview of course objectives and structure
Introduction to KerasCV and its integration with Tensorflow, PyTorch, and JAX
• KerasCV Library Basics
Setting up the environment and installing necessary libraries
Introduction to KerasCV's features and advantages
Understanding backend systems: Tensorflow, PyTorch, JAX
• Image Classification Techniques
Introduction to image classification and recognition tasks
Using pre-trained models with KerasCV for image classification
Fine-tuning and transfer learning for specific datasets and applications
• Object Detection
Introduction to object detection and its real-world applications
Utilizing pre-trained models for object detection
Fine-tuning models for specific object detection tasks
Creating custom object detection datasets using LabelImg
• Generative AI with Stable Diffusion
Introduction to text-to-image generation with Stable Diffusion
Understanding the capabilities and inner workings of Stable Diffusion
Using KerasCV for implementing and enhancing image generation
Exploring XLA compilation and mixed precision support in KerasCV
• Practical Implementation and Hands-on Projects
Step-by-step exercises for image classification
Object detection project using custom and public datasets
Creative projects with generative AI
• Course Wrap-up
Recap of learned concepts and techniques
Suggestions for further reading and exploration
Final Q&A and discussion on future trends in AI and computer vision
• Additional Resources
Recommended readings and online resources
Access to KerasCV and Stable Diffusion documentation
• Assessment and Certification
Course quizzes and practical assignments
Certificate of completion criteria and process

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Taught by

Lazy Programmer Inc. and Lazy Programmer Team

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Subjects

Computer Science