Table of Contents 1. Introduction 2. Alignment Criteria 3. Helpfulness 4. Honesty 5. Harmless 6. Collecting Human Feedback 7. Human Labeler Selection 8. Human Feedback Collection 9. Question-based Approach 10. Rule Based Approach 11. Reinforcement Learning from Human Feedback 12. Conclusion Introduction LLMs have demonstrated impressive capabilities across various NLP

YOLO (You Only Look Once) is a family of real-time object detection models for computer vision introduced in 2016 by Joseph Redmon et al. YOLO is known for its speed and accuracy in object detection, as it can detect objects in images or video frames in a single forward pass

Table of Contents 1. Introduction 2. Prerequisites 3. Concepts Required 4. Methodology 5. Dataset Selection 6. Implementation 7. Conclusion Introduction The aquaculture industry, which involves fish farming, faces various challenges that hinder its growth and sustainability. One of the major issues is the lack of efficient and accurate monitoring of

The security surveillance industry's problem is improving security systems' accuracy, efficiency, and scalability. This includes both hardware and software components. Traditionally, security surveillance systems are limited by human attention and can miss crucial events or activities. Security personnel may be unable to monitor all areas at all times, leading to

Table of Contents 1. Prerequisites 2. Transfer Learning 3. Methodology 4. Dataset Selection 5. Implementation 6. Conclusion Introduction The home services industry is facing a major problem with the accuracy and efficiency of object recognition in its operations. Home services include cleaning, maintenance, repair, and installation, which require various tools,

Driver monitoring systems are increasingly important to make on-road driving safer. To do this, the car must be able to monitor the driver's behavior and intervene if necessary to ensure the safety of everyone on the road. One of the biggest challenges in developing driver monitoring systems is the complexity

Table of Contents 1. Introduction 2. Prerequisites 3. YOLOv5 4. Methodology 5. Implementation 6. Hands-on with Code 7. Conclusion Introduction Real-time object detection is an emerging technology that has the potential to revolutionize the way we drive. The problem it addresses is the safety concern caused by unexpected objects or

Table of Contents 1. Introduction 2. Prerequisites 3. Transfer Learning 4. Methodology 5. Conclusion Introduction The problem of vehicle damage plagues the automotive industry. Vehicles are prone to damage in several ways, including accidents, collisions, natural disasters, and wear and tear. This problem affects several industries, including car manufacturers, insurers,