Machine Learning: Modern Computer Vision & Generative AI

Use KerasCV, Python, Tensorflow, PyTorch, & JAX for Image Recognition, Object Detection, and Stable Diffusion

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Course Data

Lectures: 40
Length: 6h 32m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course 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 handson experience in using pretrained models and finetuning them for specific tasks.
  • Learn to create custom object detection datasets to tackle realworld problems effectively.
  • Unlock the world of generative AI with Stable Diffusion, enabling you to generate images from text with stateoftheart 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.

Suggested Prerequisites: Basic knowledge of machine learning and Python programming. Familiarity with deep learning concepts and Keras is highly beneficial but not mandatory.

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.



3 Lectures · 09min
  1. Introduction and Outline (04:26) (FREE preview available)
  2. How to Succeed in this Course (03:04)
  3. Where to get the code (01:42)

Image Classification, Fine-Tuning and Transfer Learning

7 Lectures · 55min
  1. Classification Section Outline (03:37)
  2. Concepts: Pre-trained Image Classifier (08:33)
  3. Pre-trained Image Classifier in Python (10:13)
  4. Transfer Learning and Fine-Tuning (08:50)
  5. Fine-Tuning an Image Classifier in Python (20:24)
  6. Classification Exercise (01:04)
  7. Suggestion Box (03:10)

Object Detection

13 Lectures · 02hr 04min
  1. Object Detection Outline (08:15)
  2. Concepts: Object Detection (05:31)
  3. Decoding the Output: IoU, Non-Max Suppression, Confidence Score (09:28)
  4. Pre-trained Object Detection in Python (12:24)
  5. Focal Loss & Smooth L1 Loss (08:10)
  6. Object Detection Dataset Formats (COCO & Pascal VOC) (04:39)
  7. LabelImg Setup (01:48)
  8. LabelImg Demo (19:27)
  9. Data Augmentation (05:18)
  10. KerasCV Object Detection Dataset Format (05:38)
  11. Fine-Tuning Object Detection in Python (Built-In Dataset) (22:03)
  12. Fine-Tuning Object Detection in Python (Custom Dataset) (19:45)
  13. Object Detection Exercise (01:46)

Generative AI with Stable Diffusion

6 Lectures · 56min
  1. Stable Diffusion Outline (04:11)
  2. Generate Images with Stable Diffusion in Python (15:57)
  3. How Do Diffusion Models Work? (Optional) (09:52)
  4. Diffusion Model Architecture - UNet (Optional) (05:48)
  5. How Diffusion Models Condition on Prompts (Optional) (04:18)
  6. A Look at the Diffusion Model Source Code (Optional) (16:00)

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

2 Lectures · 37min
  1. Anaconda Environment Setup (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

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

4 Lectures · 51min
  1. Beginner's Coding Tips (13:22)
  2. How to Code Yourself (part 1) (15:55)
  3. How to Code Yourself (part 2) (09:24)
  4. Proof that using Jupyter Notebook is the same as not using it (12:29)

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

3 Lectures · 49min
  1. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  2. What order should I take your courses in? (part 1) (11:19)
  3. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)
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