Autonomous Cars: The Complete Computer Vision Course 2022
Autonomous Cars: The Complete Computer Vision Course 2022, available at $19.99, has an average rating of 4.6, with 112 lectures, based on 52 reviews, and has 320 subscribers.
You will learn about YOLO OpenCV Detection with the grayscale image Colour space techniques RGB space HSV space Sharpening and blurring Edge detection and gradient calculation Sobel Laplacian edge detector Canny edge detection Affine and Projective transformation Image translation, rotation, and resizing Hough transform Masking the region of interest Bitwise_and KNN background subtractor MOG background subtractor MeanShift Kalman filter U-NET SegNet Encoder and Decoder Pyramid Scene Parsing Network DeepLabv3+ E-Net YOLO OpenCV This course is ideal for individuals who are Beginners who are starting to learn Computer Vision. or Undergraduate students who are studying subjects related to Artificial Intelligence. or People who want to solve their own problems using Computer Vision. or Students who want to work in companies developing Computer Vision projects. or People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve. or Anyone interested in Artificial Intelligence or Computer Vision. or Data scientists who want to grow their portfolio. or Professionals who want to understand how to apply Computer Vision to real projects. or Software engineers interested in learning the algorithms that power self-driving cars. It is particularly useful for Beginners who are starting to learn Computer Vision. or Undergraduate students who are studying subjects related to Artificial Intelligence. or People who want to solve their own problems using Computer Vision. or Students who want to work in companies developing Computer Vision projects. or People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve. or Anyone interested in Artificial Intelligence or Computer Vision. or Data scientists who want to grow their portfolio. or Professionals who want to understand how to apply Computer Vision to real projects. or Software engineers interested in learning the algorithms that power self-driving cars.
Enroll now: Autonomous Cars: The Complete Computer Vision Course 2022
Summary
Title: Autonomous Cars: The Complete Computer Vision Course 2022
Price: $19.99
Average Rating: 4.6
Number of Lectures: 112
Number of Published Lectures: 112
Number of Curriculum Items: 112
Number of Published Curriculum Objects: 112
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- YOLO
- OpenCV
- Detection with the grayscale image
- Colour space techniques
- RGB space
- HSV space
- Sharpening and blurring
- Edge detection and gradient calculation
- Sobel
- Laplacian edge detector
- Canny edge detection
- Affine and Projective transformation
- Image translation, rotation, and resizing
- Hough transform
- Masking the region of interest
- Bitwise_and
- KNN background subtractor
- MOG background subtractor
- MeanShift
- Kalman filter
- U-NET
- SegNet
- Encoder and Decoder
- Pyramid Scene Parsing Network
- DeepLabv3+
- E-Net
- YOLO
- OpenCV
Who Should Attend
- Beginners who are starting to learn Computer Vision.
- Undergraduate students who are studying subjects related to Artificial Intelligence.
- People who want to solve their own problems using Computer Vision.
- Students who want to work in companies developing Computer Vision projects.
- People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve.
- Anyone interested in Artificial Intelligence or Computer Vision.
- Data scientists who want to grow their portfolio.
- Professionals who want to understand how to apply Computer Vision to real projects.
- Software engineers interested in learning the algorithms that power self-driving cars.
Target Audiences
- Beginners who are starting to learn Computer Vision.
- Undergraduate students who are studying subjects related to Artificial Intelligence.
- People who want to solve their own problems using Computer Vision.
- Students who want to work in companies developing Computer Vision projects.
- People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve.
- Anyone interested in Artificial Intelligence or Computer Vision.
- Data scientists who want to grow their portfolio.
- Professionals who want to understand how to apply Computer Vision to real projects.
- Software engineers interested in learning the algorithms that power self-driving cars.
Autonomous Cars: Computer Vision and Deep Learning
The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.
As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.
The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.
Tools and algorithms we’ll cover include:
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OpenCV.
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Deep Learning and Artificial Neural Networks.
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Convolutional Neural Networks.
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YOLO.
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HOG feature extraction.
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Detection with the grayscale image.
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Colour space techniques.
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RGB space.
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HSV space.
-
Sharpening and blurring.
-
Edge detection and gradient calculation.
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Sobel.
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Laplacian edge detector.
-
Canny edge detection.
-
Affine and Projective transformation.
-
Image translation, rotation, and resizing.
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Hough transform.
-
Masking the region of interest.
-
Bitwise_and.
-
KNN background subtractor.
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MOG background subtractor.
-
MeanShift.
-
Kalman filter.
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U-NET.
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SegNet.
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Encoder and Decoder.
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Pyramid Scene Parsing Network.
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DeepLabv3+.
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E-Net.
If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:
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Detection of road markings.
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Road Sign Detection.
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Detecting Pedestrian Project.
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Frozen Lake environment.
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Semantic Segmentation.
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Vehicle Detection.
That is all. See you in class!
“If you can’t implement it, you don’t understand it”
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Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
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My courses are the ONLY course where you will learn how to implement deep REINFORCEMENT LEARNING algorithms from scratch
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Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
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After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…
Course Curriculum
Chapter 1: Introduction (New Content)
Lecture 1: Course structure
Lecture 2: How To Make The Most Out Of This Course
Lecture 3: What is Neuron
Lecture 4: What is ANN
Lecture 5: What is Multilayer Neural Network
Lecture 6: What is keras (Optional from AI in Healthcare course)
Lecture 7: Important Terms in this course
Lecture 8: Important note about tools in this course
Lecture 9: Introduction to Self-Driving Cars
Lecture 10: Benefit of Self-Driving Cars
Lecture 11: Building the safe systems
Lecture 12: Deep learning and computer vision approaches for Self-Driving Cars
Lecture 13: LIDAR and computer vision for Self-Driving Cars vision
Chapter 2: Activation function
Lecture 1: What is activation function
Lecture 2: What is Rectified Linear Unit function
Lecture 3: What is Leaky ReLU function
Lecture 4: What is tanh function
Lecture 5: What is Softmax function
Lecture 6: What is The Exponential linear unit function
Lecture 7: What is Swish function
Lecture 8: What is sigmoid function
Lecture 9: Activation Function Implementation
Chapter 3: Basic Deep Learning Project (NEW CONTENT ADDED)
Lecture 1: Introduction to the project
Lecture 2: Importing Data and Libraries
Lecture 3: Splitting the dataset into training test and test set
Lecture 4: Standardizing data
Lecture 5: Building and compiling the model
Lecture 6: Training the model Part 1
Lecture 7: Training the model Part 2
Lecture 8: Predicting new, unseen data
Lecture 9: Evaluating the model's performance
Lecture 10: Saving and loading models
Lecture 11: Summary of the project
Chapter 4: Computer vision for Self-driving Cars (NEW CONTENT)
Lecture 1: Introduction
Lecture 2: Computer vision Introduction
Lecture 3: Challenges in computer vision
Lecture 4: Artificial eyes versus human eyes
Lecture 5: Digital representation of an image
Lecture 6: Converting images from RGB to grayscale
Lecture 7: Detection with the grayscale image
Lecture 8: Detection with the RGB image
Lecture 9: Challenges in color selection techniques and color space techniques
Lecture 10: Introduction to RGB space and HSV space
Lecture 11: Color space manipulation
Lecture 12: Introduction to convolution
Lecture 13: Introduction to Sharpening and blurring
Lecture 14: Sharpening and blurring Implementation
Lecture 15: introduction to Edge detection and gradient calculation
Lecture 16: Introduction to Sobel and the Laplacian edge detector
Lecture 17: Canny edge detection Implementation
Lecture 18: Introduction to Affine and Projective transformation
Lecture 19: Image rotation
Lecture 20: Image translation Implementation
Lecture 21: Image resizing Implementation
Lecture 22: Introduction to Perspective transformation
Lecture 23: Perspective transformation Implementation
Lecture 24: Cropping, dilating, and eroding an image Implementation
Lecture 25: Masking regions of interest
Lecture 26: Introduction to the Hough transform
Lecture 27: The Hough transform Implementation
Lecture 28: Summary of the section
Chapter 5: Detection of road markings by OpenCV (New Content)
Lecture 1: Introduction
Lecture 2: Finding road markings in a image
Lecture 3: Loading the image using OpenCV and Converting the image into grayscale
Lecture 4: Smoothing the image and Implementing Canny Edge detection
Lecture 5: Masking the region of interest
Lecture 6: bitwise_and and Hough transform implementation
Lecture 7: Optimizing the detected road markings
Lecture 8: Detecting road markings in a video
Lecture 9: Summary of the section
Chapter 6: Road Sign Detection (New Content)
Lecture 1: Introduction to convolution
Lecture 2: Pooling Layers
Lecture 3: Introduction to the project
Lecture 4: Loading the data
Lecture 5: Image exploration
Lecture 6: Data preparation
Lecture 7: Model training
Lecture 8: Model accuracy
Lecture 9: Summary
Chapter 7: Detecting Pedestrian Project (New Content)
Lecture 1: Introduction to tracking objects
Lecture 2: Background subtraction
Lecture 3: Introduction to MOG background subtractor
Lecture 4: MOG background subtractor
Lecture 5: KNN background subtractor
Lecture 6: Detecting pedestrians Introduction
Lecture 7: MeanShift Introduction
Lecture 8: Kalman filter
Lecture 9: Implementing pedestrians detection Part 1
Lecture 10: Implementing pedestrians detection Part 2
Lecture 11: Implementing pedestrians detection Part 3
Lecture 12: Implementing pedestrians detection Part 4
Lecture 13: Summary of the section
Chapter 8: Semantic Segmentation (New Content)
Instructors
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Hoang Quy La
Electrical Engineer
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 1 votes
- 3 stars: 4 votes
- 4 stars: 4 votes
- 5 stars: 37 votes
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