Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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Set up subtitle for video – Hướng dẫn thêm phụ đề
01 – Introduction
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001 Course content_part1
06:03
001 Course content_part2_part1
03:01
001 Course content_part2_part2
03:01
02 – Face detection
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001 Plan of attack
04:01
002 Images and pixels
04:27
003 Cascade classifier – intuition_part1
04:52
003 Cascade classifier – intuition_part2
04:52
004 Loading and pre-processing the image
12:05
005 Face detection with Haarcascade and OpenCV_part1
04:15
005 Face detection with Haarcascade and OpenCV_part2
04:15
005 Face detection with Haarcascade and OpenCV_part3_part1
02:07
005 Face detection with Haarcascade and OpenCV_part3_part2
02:07
006 Haarcascades parameters 1
07:05
007 Haarcascades parameters 2_part1
04:25
007 Haarcascades parameters 2_part2
04:25
008 Eye detection with haarcascades_part1
03:28
008 Eye detection with haarcascades_part2
03:28
008 Eye detection with haarcascades_part3
03:28
010 Homework solution
02:11
011 HOG (Histrograms of Oriented Gradients) – intuition
11:18
012 Face detection with HOG and Dlib_part1
05:23
012 Face detection with HOG and Dlib_part2
05:23
013 Face detection with CNN and Dlib
05:52
015 Homework solution
05:04
016 Anaconda and PyCharm
03:02
017 Face detection on the webcam_part1
04:03
017 Face detection on the webcam_part2
04:03
03 – Face recognition
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001 Plan of attack
04:30
002 LBPH algorithm – intuition
09:26
003 Loading the faces dataset
09:56
004 Preprocessing the images_part1
07:53
004 Preprocessing the images_part2
07:53
005 Training the LBPH classifier
04:38
006 Recognizing faces with LBPH
08:22
007 Evaluating the LBPH classifier
11:24
008 LBPH parameters
04:36
009 LBPH parameters – implementation
04:12
010 Detecting facial points_part1
05:53
010 Detecting facial points_part2_part1
02:56
010 Detecting facial points_part2_part2
02:56
011 Detecting facial descriptors 1_part1
07:11
011 Detecting facial descriptors 1_part2
07:11
012 Detecting facial descriptors 2_part1
07:52
012 Detecting facial descriptors 2_part2
07:52
013 Calculating distances between faces_part1
06:49
013 Calculating distances between faces_part2
06:49
014 Recognizing faces with Dlib 1_part1
05:58
014 Recognizing faces with Dlib 1_part2
05:57
015 Recognizing faces with Dlib 2
04:14
017 Homework solution
05:44
018 Face recognition on the webcam
04:48
04 – Object tracking
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001 Plan of attack
04:09
002 Object tracking vs. object detection
05:08
003 KCF and CSRT algorithms
06:47
004 Object tracking with KCF_part1
07:11
004 Object tracking with KCF_part2
07:11
005 Object tracking with CSRT
01:58
007 Homework solution
04:11
05 – Neural networks for image classification
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001 Plan of attack
03:04
002 Biological fundamentals
05:16
003 Artificial neuron
07:19
004 Perceptron
09:41
005 Weight update 1
11:29
006 Weight update 2
13:21
007 Introduction to multilayer neural networks
03:52
008 Activation functions
05:01
009 Hidden layer activation 1
05:28
010 Hidden layer activation 2
03:59
011 Output layer activation
04:40
012 Error calculation (loss function)
04:54
013 Basic algorithm
03:53
014 Gradient descent and derivative
08:56
015 Output layer delta
05:45
016 Hidden layer delta
07:27
017 Backpropagation and learning rate
06:30
018 Weight update with backprogation 1
06:23
019 Weight update with backprogation 2
07:39
020 Bias, error and multiple outputs
11:16
021 Hidden layers
10:47
022 Output layer with categorical data
04:37
023 Stochastic gradient descent
05:00
024 Deep learning
03:05
025 Pixels and neural networks
06:34
026 Importing the libraries
04:21
027 Extracting pixels from images 1
10:51
028 Extracting pixels from images 2
09:59
029 Extracting pixels from images 3
07:07
030 Extracting pixels from images 4
07:29
031 Normalizing the data
03:54
032 Creating the train and test sets
05:08
033 Building and training the neural network
11:45
034 Evaluating the neural network
12:38
035 Saving and loading the network
07:06
036 Classifying one single image
06:17
037 Extracting features from images
11:02
038 Feature extraction with OpenCV 1
06:20
039 Feature extraction with OpenCV 2_part1
07:53
039 Feature extraction with OpenCV 2_part2
07:53
040 Feature extraction with OpenCV 3
07:07
041 Feature extraction with OpenCV 4
06:57
042 Feature extraction with OpenCV 5
07:07
043 Creating the train and test sets
04:16
044 Building and training the neural network
07:29
045 Evaluating the neural network
08:35
046 Saving, loading and classifying one single image
05:16
048 Homework solution
09:27
06 – Convolutional neural networks for image classification
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001 Plan of attack
01:55
002 Introduction to convolutional neural networks
07:18
003 Convolutional operation
10:04
004 Pooling
05:29
005 Flattening
06:31
006 Dense neural network
05:10
007 Importing the libraries
03:59
008 Loading the images
04:53
009 Creating the train and test dataset_part1
05:42
009 Creating the train and test dataset_part2
05:42
010 Building and training the neural network_part1
06:58
010 Building and training the neural network_part2
06:58
011 Evaluating the neural network
08:59
012 Saving and loading the network
02:43
013 Classifying one single image
06:34
015 Homework solution_part1
05:00
015 Homework solution_part2
05:00
07 – Transfer learning and fine tuning
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001 Plan of attack
02:14
002 Transfer learning – intuition
06:41
003 Importing the libraries and dataset
05:12
004 Creating the train and test dataset
03:36
005 Pre-trained neural network
12:04
006 Creating the custom dense layer
07:32
007 Building and training the neural network
06:17
008 Evaluating the neural network
06:13
009 Fine tuning – intuition
02:44
010 Fine tuning – implementation and evaluation
06:09
011 Saving, loading and classifying one single image
03:03
013 Homework solution
09:41
08 – Neural networks for classification of emotions
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001 Plan of attack
03:44
002 Importing the libraries and images
05:35
003 Creating the train and test dataset
04:20
004 Building and training the neural network
14:18
005 Saving and loading the model
01:29
006 Evaluating the neural network
04:58
007 Classifying one single image
08:17
008 Classifying multiple images
08:02
009 Classifying emotions in videos_part1
05:42
009 Classifying emotions in videos_part2
05:42
011 Homework solution
05:39
09 – Autoencoders
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001 Plan of attack
02:34
002 Autoencoders – intuition
06:43
003 Importing the libraries and dataset
05:57
004 Visualizing the images
09:13
005 Preprocessing the images
05:28
006 Building and training a linear autoencoder
11:03
007 Encoding the images
08:29
008 Decoding the images
08:40
009 Encoding and decoding the test images
09:41
010 Convolutional autoencoders 1
06:05
011 Convolutional autoencoders 2_part1
09:09
011 Convolutional autoencoders 2_part2
09:09
012 Convolutional autoencoders 3
08:21
013 Convolutional autoencoders 4
09:28
015 Homework solution_part1
05:50
015 Homework solution_part2
05:49
10 – Object detection with YOLO
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001 Plan of attack
02:05
002 YOLO – intuition
06:07
003 Downloading and compiling Darknet
05:51
004 Testing the detector_part1
05:17
004 Testing the detector_part2
05:17
005 Darknet and GPU
08:40
006 Threshold and ext_output parameters_part1
04:03
006 Threshold and ext_output parameters_part2
04:03
007 Detecting objects in videos
07:28
009 Homework solution
02:25
11 – Recognition of gestures and actions
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001 Plan of attack
02:56
002 Gestures and actions recognition – intuition
07:02
003 Importing the libraries and the image
08:34
004 Loading the pre-trained neural network
04:32
005 Predicting body points 1_part1
08:42
005 Predicting body points 1_part2
08:42
006 Predicting body points 2
05:17
007 Detecting gestures in images_part1
05:53
007 Detecting gestures in images_part2
05:53
008 Detecting gestures in videos 1
06:06
009 Detecting gestures in videos 2
06:35
011 Homework solution
04:17
12 – Deep dream
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001 Plan of attack
02:45
002 Deep dream – intuition
06:10
003 Loading the InceptionNet network_part1
06:03
003 Loading the InceptionNet network_part2
06:03
004 Loading and preprocessing the image
08:58
005 Getting the activations
06:19
006 Calculating the loss
07:55
007 Gradient ascent 1
09:14
008 Gradient ascent 2
05:11
009 Generating images_part1_part1
01:55
009 Generating images_part1_part2
01:55
009 Generating images_part2
03:51
011 Homework solution
02:30
13 – Style transfer
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001 Plan of attack
02:50
002 Style transfer – intuition
05:45
003 Loading VGG19 network
05:56
004 Loading and pre-processing the images
10:58
005 Building the neural network 1_part1
08:17
005 Building the neural network 1_part2
08:17
006 Building the neural network 2_part1
05:21
006 Building the neural network 2_part2
05:21
007 Building the neural network 3_part1
07:34
007 Building the neural network 3_part2
07:34
008 Building the neural network 4
11:00
009 Training the neural network 1
15:40
010 Training the neural network 2_part1
07:25
010 Training the neural network 2_part2_part1
03:42
010 Training the neural network 2_part2_part2
03:42
011 Visualizing the result
03:51
013 Homework solution
03:59
14 – GANs (Generative adversarial networks)
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001 Plan of attack
03:18
002 GANs – intuition
10:48
003 Loading the dataset_part1
07:14
003 Loading the dataset_part2
07:14
004 Building the generator 1_part1
07:53
004 Building the generator 1_part2
07:53
005 Building the generator 2
06:52
006 Building the discriminator
10:19
007 Calculating the loss
08:41
008 Training the GAN 1
12:30
009 Training the GAN 2_part1
05:37
009 Training the GAN 2_part2
05:37
011 Homework solution
04:24
15 – Image segmentation
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001 Plan of attack
05:19
002 Image segmentation – intuition_part1
04:39
002 Image segmentation – intuition_part2
04:39
003 Downloading the repository
03:49
005 Importing the libraries
11:33
006 Loading the pre-trained neural network
09:01
007 Detecting objects_part1
05:34
007 Detecting objects_part2
05:34
008 Removing the background 1_part1_part1
03:12
008 Removing the background 1_part1_part2
03:11
008 Removing the background 1_part2
06:23
009 Removing the background 2
06:25
010 Segmentation in videos 1
09:17
011 Segmentation in videos 2
05:40
013 Homework solution
02:41
16 – Final remarks
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001 Final remarks
02:40
17 – Congratulations!! Don’t forget your Prize )
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Computer Vision Masterclass
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