Autonomous Cars
Deep Learning and Computer Vision in Python
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
Template matching
HOG feature extraction
Ryan Ahmed
Students of our popular course, "Data Science, Deep Learning, and Machine Learning with Python" may find some of the topics to be a review of what was covered there, seen through the lens of self-driving cars. But, most of the course focuses on topics we've never covered before, specific to computer vision techniques used in autonomous vehicles. There are plenty of new, valuable skills to be learned here.
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What people are saying
autonomous car
Porém somente introduziu o assinto sobre Autonomous Car.
I wanted to learn more about autonomous car, but it turned out to be a simple CNN classification with just cars as the subject.
The examples are centered around autonomous vehicles, specially the vision from cameras so there's no examples of working with the other sensors in an autonomous car.
Don't expect to learn how to build and autonomous car here, but rather get an idea how some things are done.
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easy to follow
Sun-dog purses with Frank Kane are always thorough and easy to follow.
Explanations of the provided code is clear and easy to follow.
The course was excellent, easy to follow, the instructor was experienced and enthisatic about the subject Very good It would be good to cover some more advanced topics as part of this course like hyper parameter tuning and more use-cases.
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deep learning
The course information is solid and I'm coming away with a much better understanding of python, Neural Networks, Deep Learning, image recognition and the tools that are used for Autonomous cars.
I would highly recommend others to take this course One of the best courses on Deep Learning!
This is a very basic/entry level machine learning and deep learning course.
I've taken other deep learning courses, so while there was a lot of overlap (naturally), I still learned a lot of things that I didn't know before, even if it was just another way of explaining things to give a more complete overview of a topic.
As I had already a background in Python and machine/deep learning, I think this course has a perfect format to dive into this fascinating topic.
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autonomous cars
The course instructors are very experienced and highly enthusiastic in sharing knowledge and helping others which helped me gain a lot of insights into Autonomous Cars.
It covers little about autonomous cars.
Everything was done with images, but when you think of autonomous cars you think video, right?
I think they titled this course to get people to enroll because there are only a couple of courses on udemy with Autonomous Cars in the title.
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machine learning
The only thing I would change is (If i were the instructor) I would force students to engage more by writing the code involved with image processing and machine learning.
from the data preprocessing to machine learning concepts in the CV.
Frank's teaching style and pleasing voice make it easy to focus on an otherwise comparatively dry and complex topic such as AI and Machine Learning.
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computer vision
I learn too many details and examples from this class Thank you for all Mostrou as técnicas sobre Deep Learning e Computer Vision.
So it's rather about "how" than "what" or "why" to do certain stuff, especially computer vision basics.
Yes I was interested about computer vision and could able to see the demo as well I'd been waiting for a course like this and 10 minutes in, i've run my first script for edge detection via my webcam.
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looking forward
Very interesting so far, looking forward to where this all leads!
I am looking forward to the sequels.
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frank kane
I have taken courses from Frank Kane multiple times now, I highly recommend his courses.
Too Basic Yes A course from Frank Kane is guarantee of success!
insights into
Good insights into the topics.
May be a decent course for someone who wants to quickly get insights into tools and available algorithms, but it will definitely not teach you the backgrounds.
'conda install
Ran into a few dependency issues early ('conda install pillow' and 'pip install cython').
Also, running the jupyter notebook using different conda environments required the use of the 'conda install -n <env> nb_kernels' and 'conda install -n <env> ipykernel' to toggle between environments (opencv conflicted with my base packages and failed to install - so used a clean install into a separate environment.
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