[Last updated 3/2024] Computer Vision Masterclass (Udemy – Engsub)
About Course
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What you’ll learn:
Understand the basic intuition about Cascade and HOG classifiers to detect faces
Implement face detection using OpenCV and Dlib library
Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
Detect faces using images and the webcam
Understand the basic intuition about LBPH algorithm to recognize faces
Implement face recognition using OpenCV and Dlib library
Recognize faces using images and the webcam
Understand the basic intuition about KCF and CSRT algorithms to perform object tracking
Learn how to track objects in videos using OpenCV library
v.v…
Description:
Computer Vision (Thị giác máy tính)
là một lĩnh vực phụ của Trí tuệ nhân tạo tập trung vào việc tạo ra các hệ thống có thể xử lý, phân tích và xác định dữ liệu hình ảnh theo cách tương tự như mắt người. Có nhiều ứng dụng thương mại trong các bộ phận khác nhau, chẳng hạn như: an ninh, tiếp thị, ra quyết định và sản xuất. Điện thoại thông minh sử dụng
Computer Vision
để mở khóa các thiết bị bằng nhận dạng khuôn mặt, xe tự lái sử dụng nó để phát hiện người đi bộ và giữ khoảng cách an toàn với các xe khác, cũng như camera an ninh sử dụng nó để xác định xem có người trong môi trường để báo động hay không được kích hoạt.
Link gốc:
https://www.udemy.com/course/computer-vision-masterclass/
Time Course:
31.5 hours (19 Lectures + Documents)
Instructor
: The R9ck
Total Weight:
12 GB
** Note
:
Chú ý:
Course Content
01 – Introduction
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12:05
02 – Face detection
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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
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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
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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
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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
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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
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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)
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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
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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
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001 Final remarks.mp4
02:39
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