Modern Computer Vision & Generative AI with Machine Learning Online Course

Modern Computer Vision & Generative AI with Machine Learning Online Course

This course is designed for anyone eager to master computer vision and generative AI. You’ll start with image recognition and object detection using KerasCV, Python, TensorFlow, PyTorch, and JAX, learning to fine-tune pre-trained models and create custom datasets with tools like LabelImg. The course also explores generative AI, focusing on Stable Diffusion to generate detailed images from text. From foundational concepts to advanced techniques, you’ll gain practical skills to tackle real-world challenges and unlock creative possibilities in AI.

Who should take this course?

This course is ideal for AI enthusiasts, machine learning practitioners, and developers who want to master computer vision and generative AI techniques. It’s also suitable for students and professionals looking to build advanced AI applications involving image generation, recognition, and real-world ML solutions.

What you will learn

• Harness the power of the KerasCV library for efficient deep learning
• Master image classification techniques using pre-trained models
• Implement object detection in real-world scenarios
• Fine-tune models for tailored applications and datasets
• Create custom object detection datasets with LabelImg
• Understand the integration of TensorFlow, PyTorch, and JAX in computer vision

Course Outline

Welcome

• Introduction and Outline
• How to Succeed in This Course
• Where to Get the Code

Image Classification, Fine-Tuning and Transfer Learning

• Classification Section Outline
• Concepts: Pre-trained Image Classifier
• Pre-trained Image Classifier in Python
• Transfer Learning and Fine-Tuning
• Fine-Tuning an Image Classifier in Python
• Classification Exercise
• Suggestion Box

Object Detection

• Object Detection Outline
• Concepts: Object Detection
• Decoding the Output: IoU, Non-Max Suppression, Confidence Score
• Pre-trained Object Detection in Python
• Focal Loss & Smooth L1 Loss
• Object Detection Dataset Formats (COCO & Pascal VOC)
• LabelImg Setup
• LabelImg Demo
• Data Augmentation
• KerasCV Object Detection Dataset Format
• Fine-Tuning Object Detection in Python (Built-In Dataset)
• Fine-Tuning Object Detection in Python (Custom Dataset)
• Object Detection Exercise

Generative AI with Stable Diffusion

• Stable Diffusion Outline
• Generate Images with Stable Diffusion in Python
• How Do Diffusion Models Work? (Optional)
• Diffusion Model Architecture – Unet
• How Diffusion Models Condition on Prompts (Optional)
• A Look at the Diffusion Model Source Code (Optional)

Setting Up Your Environment (Appendix/FAQ by Student Request)

• Anaconda Environment Setup
• How to Install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

• Beginner's Coding Tips
• How to Code Yourself (Part 1)
• How to Code Yourself (Part 2)
• Proof that using Jupyter Notebook is the same as not using it

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

• What order should I take your courses in? (part 1)
• What order should I take your courses in? (part 2)

Appendix / FAQ Finale

• Where to Get Discount Coupons and FREE Deep Learning Material?