Machine Learning- Modern Computer Vision Generative AI

Machine Learning: Modern Computer Vision & Generative AI, available at $109.99, has an average rating of 4.71, with 41 lectures, based on 325 reviews, and has 3244 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner to advanced students and professionals interested in computer vision with KerasCV It is particularly useful for Beginner to advanced students and professionals interested in computer vision with KerasCV.

Enroll now: Machine Learning: Modern Computer Vision & Generative AI

Summary

Title: Machine Learning: Modern Computer Vision & Generative AI

Price: $109.99

Average Rating: 4.71

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Beginner to advanced students and professionals interested in computer vision with KerasCV

Target Audiences

Beginner to advanced students and professionals interested in computer vision with 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction & Outline

Lecture 2: How to Succeed in This Course

Lecture 3: Where to Get the Code

Chapter 2: Image Classification, Fine-Tuning and Transfer Learning

Lecture 1: Classification Section Outline

Lecture 2: Concepts: Pre-trained Image Classifier

Lecture 3: Pre-trained Image Classifier in Python

Lecture 4: Transfer Learning and Fine-Tuning

Lecture 5: Fine-Tuning an Image Classifier in Python

Lecture 6: Classification Exercise

Lecture 7: Suggestion Box

Chapter 3: Object Detection

Lecture 1: Object Detection Outline

Lecture 2: Concepts: Object Detection

Lecture 3: Decoding the Output: IoU, Non-Max Suppression, Confidence Score

Lecture 4: Pre-trained Object Detection in Python

Lecture 5: Focal Loss & Smooth L1 Loss

Lecture 6: Object Detection Dataset Formats (COCO & Pascal VOC)

Lecture 7: LabelImg Setup

Lecture 8: LabelImg Demo

Lecture 9: Data Augmentation

Lecture 10: KerasCV Object Detection Dataset Format

Lecture 11: Fine-Tuning Object Detection in Python (Built-In Dataset)

Lecture 12: Fine-Tuning Object Detection in Python (Custom Dataset)

Lecture 13: Object Detection Exercise

Chapter 4: Generative AI with Stable Diffusion

Lecture 1: Stable Diffusion Outline

Lecture 2: Generate Images with Stable Diffusion in Python

Lecture 3: How Do Diffusion Models Work? (Optional)

Lecture 4: Diffusion Model Architecture (Optional)

Lecture 5: How Diffusion Models Condition on Prompts (Optional)

Lecture 6: A Look at the Diffusion Model Source Code (Optional)

Chapter 5: Setting Up Your Environment (Appendix/FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 6: Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

Lecture 1: Beginners Coding Tips

Lecture 2: How to Code Yourself (part 1)

Lecture 3: How to Code Yourself (part 2)

Lecture 4: Proof that using Jupyter Notebook is the same as not using it

Chapter 7: Effective Learning Strategies for Machine Learning (Appendix/FAQ)

Lecture 1: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

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

Lecture 3: What order should I take your courses in? (part 2)

Chapter 8: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 153 votes

5 stars: 167 votes

Frequently Asked Questions

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