Machine Learning: Modern Computer Vision & Generative Ai
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Machine Learning: Modern Computer Vision & Generative Ai
Published 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.97 GB | Duration: 4h 6m
Use KerasCV, Python, Tensorflow, PyTorch, & JAX for Image Recognition, Object Detection, and Stable Diffusion
What you'll learn
Computer vision with KerasCV
How to do image classification / image recognition with a pretrained model and fine-tuning / transfer learning
How to do object detection with a pretrained model and fine-tuning / transfer learning
How to generate images with Stable Diffusion in KerasCV
Requirements
Experience with Keras
Description
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.
Overview
Section 1: Introduction
Lecture 1 Introduction & Outline
Lecture 2 How to Succeed in This Course
Lecture 3 Where to Get the Code
Section 2: Image Classification, Fine-Tuning and Transfer Learning
Lecture 4 Classification Section Outline
Lecture 5 Concepts: Pre-trained Image Classifier
Lecture 6 Pre-trained Image Classifier in Python
Lecture 7 Transfer Learning and Fine-Tuning
Lecture 8 Fine-Tuning an Image Classifier in Python
Lecture 9 Classification Exercise
Lecture 10 Suggestion Box
Section 3: Object Detection
Lecture 11 Object Detection Outline
Lecture 12 Concepts: Object Detection
Lecture 13 Decoding the Output: IoU, Non-Max Suppression, Confidence Score
Lecture 14 Pre-trained Object Detection in Python
Lecture 15 Focal Loss & Smooth L1 Loss
Lecture 16 Object Detection Dataset Formats (COCO & Pascal VOC)
Lecture 17 LabelImg Setup
Lecture 18 LabelImg Demo
Lecture 19 Data Augmentation
Lecture 20 KerasCV Object Detection Dataset Format
Lecture 21 Fine-Tuning Object Detection in Python (Built-In Dataset)
Lecture 22 Fine-Tuning Object Detection in Python (Custom Dataset)
Lecture 23 Object Detection Exercise
Section 4: Generative AI with Stable Diffusion
Lecture 24 Stable Diffusion Outline
Lecture 25 Generate Images with Stable Diffusion in Python
Lecture 26 How Do Diffusion Models Work? (Optional)
Lecture 27 Diffusion Model Architecture (Optional)
Lecture 28 How Diffusion Models Condition on Prompts (Optional)
Lecture 29 A Look at the Diffusion Model Source Code (Optional)
Beginner to advanced students and professionals interested in computer vision with KerasCV
Screenshots
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