Machine Learning: Modern Computer Vision & Generative AI

  • Welcome
  • Introduction and Outline
Introduction and Outline
04:26
How to Succeed in this Course
03:04
Where to get the code
02:06
Classification Section Outline
03:37
Concepts: Pre-trained Image Classifier
08:33
Pre-trained Image Classifier in Python
10:13
Transfer Learning and Fine-Tuning
08:50
Fine-Tuning an Image Classifier in Python
20:24
Classification Exercise
01:04
Suggestion Box
03:10
Object Detection Outline
08:15
Concepts: Object Detection
05:31
Decoding the Output: IoU, Non-Max Suppression, Confidence Score
09:28
Pre-trained Object Detection in Python
12:24
Focal Loss & Smooth L1 Loss
08:10
Object Detection Dataset Formats (COCO & Pascal VOC)
04:39
LabelImg Setup
01:48
LabelImg Demo
19:27
Data Augmentation
05:18
KerasCV Object Detection Dataset Format
05:38
Fine-Tuning Object Detection in Python (Built-In Dataset)
22:03
Fine-Tuning Object Detection in Python (Custom Dataset)
19:45
Object Detection Exercise
01:46
Stable Diffusion Outline
04:11
Generate Images with Stable Diffusion in Python
15:57
How Do Diffusion Models Work? (Optional)
09:52
Diffusion Model Architecture - UNet (Optional)
05:48
How Diffusion Models Condition on Prompts (Optional)
04:18
A Look at the Diffusion Model Source Code (Optional)
16:00
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Beginner's Coding Tips
13:22
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
How to use Github & Extra Coding Tips (Optional)
11:12
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49
Introduction and Outline
04:26
How to Succeed in this Course
03:04
Where to get the code
02:06
Classification Section Outline
03:37
Concepts: Pre-trained Image Classifier
08:33
Pre-trained Image Classifier in Python
10:13
Transfer Learning and Fine-Tuning
08:50
Fine-Tuning an Image Classifier in Python
20:24
Classification Exercise
01:04
Suggestion Box
03:10
Object Detection Outline
08:15
Concepts: Object Detection
05:31
Decoding the Output: IoU, Non-Max Suppression, Confidence Score
09:28
Pre-trained Object Detection in Python
12:24
Focal Loss & Smooth L1 Loss
08:10
Object Detection Dataset Formats (COCO & Pascal VOC)
04:39
LabelImg Setup
01:48
LabelImg Demo
19:27
Data Augmentation
05:18
KerasCV Object Detection Dataset Format
05:38
Fine-Tuning Object Detection in Python (Built-In Dataset)
22:03
Fine-Tuning Object Detection in Python (Custom Dataset)
19:45
Object Detection Exercise
01:46
Stable Diffusion Outline
04:11
Generate Images with Stable Diffusion in Python
15:57
How Do Diffusion Models Work? (Optional)
09:52
Diffusion Model Architecture - UNet (Optional)
05:48
How Diffusion Models Condition on Prompts (Optional)
04:18
A Look at the Diffusion Model Source Code (Optional)
16:00
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Beginner's Coding Tips
13:22
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
How to use Github & Extra Coding Tips (Optional)
11:12
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49

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