Modern Computer Vision & Generative AI with Machine Learning Practice Exam
Modern Computer Vision & Generative AI with Machine Learning Exam
This comprehensive course is designed to empower learners at every level. From mastering image classification to diving deep into generative AI techniques, this course equips you with the tools and knowledge to excel in real-world applications. Featuring hands-on experience with cutting-edge technologies like Stable Diffusion and robust libraries such as KerasCV, TensorFlow, PyTorch, and JAX, this course blends theory with practical implementation for maximum impact.
Who should take the exam?
This course is ideal for learners at all levels with a foundational grasp of machine learning and Python programming. Whether you're a beginner eager to explore computer vision or a professional looking to enhance your skills in generative AI, this course offers a structured and engaging learning experience.
Knowledge Gained
- In-depth understanding of computer vision principles, including image classification and object detection.
- Hands-on expertise in fine-tuning pre-trained models and creating custom datasets.
- Proficiency in generative AI techniques, with a special focus on Stable Diffusion.
- Practical insights into leveraging deep learning frameworks like KerasCV for real-world applications.
Skills Required
- Basic knowledge of Python programming.
- Foundational understanding of machine learning concepts.
- Willingness to engage with both theoretical and hands-on learning.
Key Benefits
- Comprehensive coverage of computer vision and generative AI, bridging foundational concepts with advanced techniques.
- Practical, hands-on learning with tools and libraries used in the industry.
- Real-world exercises to build confidence and proficiency.
- Lifetime access to course materials with regular updates to stay current.
- A focused approach to mastering Stable Diffusion for generative art.
Course Contents
The Modern Computer Vision & Generative AI with Machine Learning Exam covers the following topics
Chapter 1: Image Classification, Fine-Tuning, and Transfer Learning
- Concepts: Pre-trained Image Classifier
- Implementing Pre-trained Image Classifier in Python
- Transfer Learning and Fine-Tuning Techniques
- Fine-Tuning an Image Classifier in Python
- Classification Exercises
Chapter 2: Object Detection
- Concepts: Object Detection and Dataset Formats (COCO & Pascal VOC)
- Decoding Outputs: IoU, Non-Max Suppression, Confidence Score
- Pre-trained Object Detection in Python
- Loss Functions: Focal Loss & Smooth L1 Loss
- Setting Up and Using LabelImg for Annotation
- Data Augmentation Techniques
- Fine-Tuning Object Detection Models with Built-In and Custom Datasets
- Object Detection Exercises
Chapter 3: Generative AI with Stable Diffusion
- Generating Images with Stable Diffusion in Python
- Understanding Diffusion Models (Optional)
- Exploring Diffusion Model Architecture – Unet
- Conditioning Diffusion Models on Prompts (Optional)
- Examining Diffusion Model Source Code (Optional)
Chapter 4: Setting Up Your Environment (Appendix/FAQ by Student Request)
- Anaconda Environment Setup
- Installing Essential Libraries: Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Chapter 5: Extra Help with Python Coding for Beginners (Appendix/FAQ by Student Request)
- Beginner's Coding Tips
- How to Code Yourself (Parts 1 & 2)
- Jupyter Notebook Demonstrations
Chapter 6: Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)
- Determining the Optimal Order for Course Progression (Parts 1 & 2)
What you will Learn?
- Mastering the KerasCV library for efficient deep learning.
- Implementing image classification with pre-trained models.
- Performing object detection for practical applications.
- Fine-tuning models to create custom solutions.
- Generating detailed images from text using Stable Diffusion.
- Integrating TensorFlow, PyTorch, and JAX into computer vision projects.
