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Computer Vision Applications with Deep Learning
Welcome
Introduction (2:35)
Special Offer! Get the VIP version of this course (1:14)
Outline and Perspective (6:49)
Where to get help (2:06)
Review
Review of CNNs (10:34)
Where to get the code and data (2:26)
Fashion MNIST (3:29)
Review of CNNs in Code (6:09)
VGG and Transfer Learning
VGG Section Intro (3:04)
What's so special about VGG? (7:00)
Transfer Learning (8:22)
Relationship to Greedy Layer-Wise Pretraining (2:19)
Getting the data (2:17)
Code pt 1 (9:23)
Code pt 2 (3:41)
Code pt 3 (3:27)
VGG Section Summary (1:47)
ResNet (and Inception)
ResNet Section Intro (2:49)
ResNet Architecture (12:45)
Building ResNet - Strategy (2:25)
Building ResNet - Conv Block Details (3:34)
Building ResNet - Conv Block Code (6:08)
Building ResNet - Identity Block Details (1:23)
Building ResNet - First Few Layers (2:27)
Building ResNet - First Few Layers (Code) (4:15)
Building ResNet - Putting it all together (4:19)
Exercise: Apply ResNet (1:16)
Applying ResNet (2:39)
1x1 Convolutions (4:03)
Optional: Inception (6:47)
Different sized images using the same network (4:12)
ResNet Section Summary (2:27)
Object Detection (SSD)
SSD Section Intro (5:04)
Object Localization (6:36)
What is Object Detection? (2:53)
How would you find an object in an image? (8:40)
The Problem of Scale (3:47)
The Problem of Shape (3:52)
SSD in Tensorflow (9:57)
Modifying SSD to work on Video (5:04)
Optional: Intersection over Union & Non-max Suppression (5:06)
SSD Section Summary (2:52)
Neural Style Transfer
Style Transfer Section Intro (2:52)
Style Transfer Theory (11:23)
Optimizing the Loss (8:02)
Code pt 1 (7:46)
Code pt 2 (7:13)
Code pt 3 (3:50)
Style Transfer Section Summary (2:21)
Facial Recognition
Facial Recognition Section Introduction (3:38)
Siamese Networks (10:17)
Code Outline (5:01)
Loading in the data (4:40)
Splitting the data into train and test (4:24)
Converting the data into pairs (5:02)
Generating Generators (4:20)
Creating the model and loss (3:12)
Accuracy and imbalanced classes (7:07)
Facial Recognition Section Summary (3:28)
Basics Review
(Review) Keras Discussion (6:48)
(Review) Keras Neural Network in Code (6:37)
(Review) Keras Functional API (4:26)
(Review) How to easily convert Keras into Tensorflow 2.0 code (1:49)
Appendix
What is the Appendix? (2:48)
Windows-Focused Environment Setup 2018 (20:20)
How to How to install Numpy, Theano, Tensorflow, etc... (17:30)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
How to Succeed in this Course (Long Version) (10:24)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
Proof that using Jupyter Notebook is the same as not using it (12:29)
Python 2 vs Python 3 (4:38)
What order should I take your courses in? (part 1) (11:18)
What order should I take your courses in? (part 2) (16:07)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
Splitting the data into train and test
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