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Image Processing and Analysis Bootcamp With OpenCV and Deep Learning in Python
Welcome to Image Processing And Analysis in Python
Brief Introduction to the Course (2:31)
Data and Code
Get Started With the Python Data Science Environment (10:57)
For Mac Users (4:05)
Introduction to iPython/Jupyter (19:13)
Working With Colabs (7:13)
Getting Started With Basic Image Processing in Python
What Are Images? (4:54)
Read in Images in Python (7:46)
Some Basic Image Conversions (3:07)
Basic Image Resizing (4:01)
Basic Image Resizing (4:01)
What is Interpolation? A Geographic Perspective (5:08)
Basic Image Transformations (6:08)
Contrast Stretching (6:20)
Filtering Images (6:21)
Introduction to Computer Vision
What is Computer Vision? (4:54)
Read in Images Using OpenCV (5:59)
Image Filtering With OpenCV (7:30)
Edge Detection With OpenCV (5:19)
More Edge Detection: Sobel Method (3:25)
Corner Detection (1:31)
Face Detection With Haar Features: Theory (5:42)
Face Detection (5:32)
Introduction to Some Concepts
What is Machine Learning? (5:32)
Unsupervised Learning Methods
What is Unsupervised Learning? (1:38)
Theory Behind PCA (2:37)
Implement PCA on Images (4:36)
PCA For Image reconstruction (4:14)
Randomised PCA (2:45)
Theory Behind K-means (1:57)
K-Means For Image Reconstruction (1:48)
Classify High Dimensional Data With t-SNE (4:55)
Practical Case Study: Identify Flowers (3:09)
Cluster the Flowers: Read in Images (7:45)
Implement PCA (4:04)
Implement t-SNE (2:25)
Supervised Learning: Classifying Images
Brief Introduction to Supervised Learning (10:10)
Implement SVM to Classify Digits (7:00)
Accuracy Assessment (9:42)
rf (4:19)
Start With Deep Learning
Why Deep Learning? (9:51)
Tensorflow Installation (15:12)
Written Tensorflow Installation Instructions
Install Keras on Windows 10 (5:16)
Install Keras on Mac (4:19)
Written Keras Installation Instructions
Deep Learning For Image Classification
Introduction to CNN (11:25)
Implement a CNN for Multi-Class Supervised Classification (7:27)
More on CNN (4:36)
Pre-Requisite For Working With Imagery Data (2:33)
CNN on Image Data-Part 1 (10:41)
CNN on Image Data-Part 2 (6:38)
More on TFLearn (7:54)
CNN Workflow for Keras (4:04)
CNN With Keras (4:10)
CNN on Image Data with Keras-Part 1 (2:27)
CNN on Image Data with Keras-Part 2 (5:05)
Transfer Learning
What is Transfer Learning? (7:41)
Implement a Pre-Built Transfer Learning Model (6:57)
Unsupervised Deep Learning
Simple Autoencoders (5:43)
Add Sparsity Constraint (4:32)
Implement a Pre-Built Transfer Learning Model
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