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:

OpenCV.

Deep Learning and Artificial Neural Networks.

Convolutional Neural Networks.

YOLO.

HOG feature extraction.

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.

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:

Detection of road markings.

Road Sign Detection.

Detecting Pedestrian Project.

Frozen Lake environment.

Semantic Segmentation.

Vehicle Detection.

That is all. See you in class!

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

My courses are the ONLY course where you will learn how to implement deep REINFORCEMENT LEARNING algorithms from scratch

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?

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 models 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

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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