Course Content
01 – Introduction
0/1
001 Course content.mp4
12:05
02 – Face detection
0/15
001 Plan of attack.mp4
04:01
002 Images and pixels.mp4
04:27
003 Cascade classifier – intuition.mp4
09:45
004 Loading and pre-processing the image.mp4
12:05
005 Face detection with Haarcascade and OpenCV.mp4
12:45
006 Haarcascades parameters 1.mp4
07:04
007 Haarcascades parameters 2.mp4
08:50
008 Eye detection with haarcascades.mp4
10:24
010 Homework solution.mp4
02:11
011 HOG (Histrograms of Oriented Gradients) – intuition.mp4
11:18
012 Face detection with HOG and Dlib.mp4
10:47
013 Face detection with CNN and Dlib.mp4
05:52
015 Homework solution.mp4
05:03
016 Anaconda and PyCharm.mp4
03:01
017 Face detection on the webcam.mp4
08:07
03 – Face recognition
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009 LBPH parameters – implementation.mp4
04:12
018 Face recognition on the webcam.mp4
04:48
017 Homework solution.mp4
05:44
015 Recognizing faces with Dlib 2.mp4
04:14
014 Recognizing faces with Dlib 1.mp4
11:55
013 Calculating distances between faces.mp4
13:38
012 Detecting facial descriptors 2.mp4
15:44
011 Detecting facial descriptors 1.mp4
14:22
010 Detecting facial points.mp4
11:46
001 Plan of attack.mp4
04:30
008 LBPH parameters.mp4
04:36
007 Evaluating the LBPH classifier.mp4
11:24
006 Recognizing faces with LBPH.mp4
08:22
005 Training the LBPH classifier.mp4
04:38
004 Preprocessing the images.mp4
15:47
003 Loading the faces dataset.mp4
09:56
002 LBPH algorithm – intuition.mp4
09:25
001 Plan of attack.mp4
04:30
04 – Object tracking
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001 Plan of attack.mp4
04:08
002 Object tracking vs. object detection.mp4
05:08
003 KCF and CSRT algorithms.mp4
06:47
004 Object tracking with KCF.mp4
14:22
005 Object tracking with CSRT.mp4
01:57
007 Homework solution.mp4
04:11
05 – Neural networks for image classification
0/47
036 Classifying one single image.mp4
06:17
025 Pixels and neural networks.mp4
06:33
026 Importing the libraries.mp4
04:21
027 Extracting pixels from images 1.mp4
10:51
028 Extracting pixels from images 2.mp4
09:59
029 Extracting pixels from images 3.mp4
07:07
030 Extracting pixels from images 4.mp4
07:29
031 Normalizing the data.mp4
03:54
032 Creating the train and test sets.mp4
05:07
033 Building and training the neural network.mp4
11:45
034 Evaluating the neural network.mp4
12:37
035 Saving and loading the network.mp4
07:06
024 Deep learning.mp4
03:05
037 Extracting features from images.mp4
11:02
038 Feature extraction with OpenCV 1.mp4
06:20
039 Feature extraction with OpenCV 2.mp4
15:47
040 Feature extraction with OpenCV 3.mp4
07:07
041 Feature extraction with OpenCV 4.mp4
06:56
042 Feature extraction with OpenCV 5.mp4
07:07
043 Creating the train and test sets.mp4
04:16
044 Building and training the neural network.mp4
07:29
045 Evaluating the neural network.mp4
08:34
046 Saving, loading and classifying one single image.mp4
05:16
048 Homework solution.mp4
09:27
013 Basic algorithm.mp4
03:53
002 Biological fundamentals.mp4
05:16
003 Artificial neuron.mp4
07:19
004 Perceptron.mp4
09:41
005 Weight update 1.mp4
11:28
006 Weight update 2.mp4
13:21
007 Introduction to multilayer neural networks.mp4
03:52
008 Activation functions.mp4
05:01
009 Hidden layer activation 1.mp4
05:28
010 Hidden layer activation 2.mp4
03:59
011 Output layer activation.mp4
04:40
012 Error calculation (loss function).mp4
04:53
001 Plan of attack.mp4
03:04
014 Gradient descent and derivative.mp4
08:56
015 Output layer delta.mp4
05:45
016 Hidden layer delta.mp4
07:27
017 Backpropagation and learning rate.mp4
06:30
018 Weight update with backprogation 1.mp4
06:23
019 Weight update with backprogation 2.mp4
07:39
020 Bias, error and multiple outputs.mp4
11:16
021 Hidden layers.mp4
10:47
022 Output layer with categorical data.mp4
04:36
023 Stochastic gradient descent.mp4
05:00
06 – Convolutional neural networks for image classification
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001 Plan of attack.mp4
01:55
002 Introduction to convolutional neural networks.mp4
07:18
003 Convolutional operation.mp4
10:04
004 Pooling.mp4
05:29
005 Flattening.mp4
06:31
006 Dense neural network.mp4
05:10
007 Importing the libraries.mp4
03:59
008 Loading the images.mp4
04:53
009 Creating the train and test dataset.mp4
11:25
010 Building and training the neural network.mp4
13:57
011 Evaluating the neural network.mp4
08:59
012 Saving and loading the network.mp4
02:43
013 Classifying one single image.mp4
06:34
015 Homework solution.mp4
10:00
07 – Transfer learning and fine tuning
0/12
001 Plan of attack.mp4
02:14
002 Transfer learning – intuition.mp4
06:41
003 Importing the libraries and dataset.mp4
05:12
004 Creating the train and test dataset.mp4
03:36
005 Pre-trained neural network.mp4
12:04
006 Creating the custom dense layer.mp4
07:32
007 Building and training the neural network.mp4
06:17
008 Evaluating the neural network.mp4
06:13
009 Fine tuning – intuition.mp4
02:44
010 Fine tuning – implementation and evaluation.mp4
06:09
011 Saving, loading and classifying one single image.mp4
03:02
013 Homework solution.mp4
09:41
08 – Neural networks for classification of emotions
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001 Plan of attack.mp4
03:44
002 Importing the libraries and images.mp4
05:35
003 Creating the train and test dataset.mp4
04:19
004 Building and training the neural network.mp4
14:18
005 Saving and loading the model.mp4
01:28
006 Evaluating the neural network.mp4
04:58
007 Classifying one single image.mp4
08:17
008 Classifying multiple images.mp4
08:02
009 Classifying emotions in videos.mp4
11:24
011 Homework solution.mp4
05:39
09 – Autoencoders
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001 Plan of attack.mp4
02:34
002 Autoencoders – intuition.mp4
06:43
003 Importing the libraries and dataset.mp4
05:57
004 Visualizing the images.mp4
09:13
005 Preprocessing the images.mp4
05:28
006 Building and training a linear autoencoder.mp4
11:03
007 Encoding the images.mp4
08:29
008 Decoding the images.mp4
08:40
009 Encoding and decoding the test images.mp4
09:41
010 Convolutional autoencoders 1.mp4
06:05
011 Convolutional autoencoders 2.mp4
18:18
012 Convolutional autoencoders 3.mp4
08:21
013 Convolutional autoencoders 4.mp4
09:28
015 Homework solution.mp4
11:39
10 – Object detection with YOLO
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001 Plan of attack.mp4
02:04
002 YOLO – intuition.mp4
06:07
003 Downloading and compiling Darknet.mp4
05:51
004 Testing the detector.mp4
10:34
005 Darknet and GPU.mp4
08:40
006 Threshold and ext_output parameters.mp4
08:07
007 Detecting objects in videos.mp4
07:28
009 Homework solution.mp4
02:25
11 – Recognition of gestures and actions
0/10
001 Plan of attack.mp4
02:56
002 Gestures and actions recognition – intuition.mp4
07:02
003 Importing the libraries and the image.mp4
08:34
004 Loading the pre-trained neural network.mp4
04:31
005 Predicting body points 1.mp4
17:25
006 Predicting body points 2.mp4
05:17
007 Detecting gestures in images.mp4
11:47
008 Detecting gestures in videos 1.mp4
06:06
009 Detecting gestures in videos 2.mp4
06:35
011 Homework solution.mp4
04:17
12 – Deep dream
0/10
001 Plan of attack.mp4
02:45
002 Deep dream – intuition.mp4
06:10
003 Loading the InceptionNet network.mp4
12:05
004 Loading and preprocessing the image.mp4
08:58
005 Getting the activations.mp4
06:19
006 Calculating the loss.mp4
07:55
007 Gradient ascent 1.mp4
09:14
008 Gradient ascent 2.mp4
05:11
009 Generating images.mp4
07:43
011 Homework solution.mp4
02:30
13 – Style transfer
0/12
001 Plan of attack.mp4
02:50
002 Style transfer – intuition.mp4
05:44
003 Loading VGG19 network.mp4
05:56
004 Loading and pre-processing the images.mp4
10:57
005 Building the neural network 1.mp4
16:35
006 Building the neural network 2.mp4
10:42
007 Building the neural network 3.mp4
15:07
008 Building the neural network 4.mp4
11:00
009 Training the neural network 1.mp4
15:40
010 Training the neural network 2.mp4
14:51
011 Visualizing the result.mp4
03:51
013 Homework solution.mp4
03:59
14 – GANs (Generative adversarial networks)
0/10
001 Plan of attack.mp4
03:18
002 GANs – intuition.mp4
10:47
003 Loading the dataset.mp4
14:29
004 Building the generator 1.mp4
15:46
005 Building the generator 2.mp4
06:52
006 Building the discriminator.mp4
10:19
007 Calculating the loss.mp4
08:41
008 Training the GAN 1.mp4
12:30
009 Training the GAN 2.mp4
11:15
011 Homework solution.mp4
04:24
15 – Image segmentation
0/11
001 Plan of attack.mp4
05:18
002 Image segmentation – intuition.mp4
09:18
003 Downloading the repository.mp4
03:49
005 Importing the libraries.mp4
11:33
006 Loading the pre-trained neural network.mp4
09:00
007 Detecting objects.mp4
11:08
008 Removing the background 1.mp4
12:47
009 Removing the background 2.mp4
06:25
010 Segmentation in videos 1.mp4
09:17
011 Segmentation in videos 2.mp4
05:40
013 Homework solution.mp4
02:41
16 – Final remarks
0/1
001 Final remarks.mp4
02:39
[Last updated 3/2024] Computer Vision Masterclass (Udemy – Engsub)
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