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Autonomous Cars: The Complete Computer Vision Course 2021

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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.


Implementing Real-time Object Detection System using PyTorch and OpenCV

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The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade. Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together. I know some of you might be thinking why I am using Python, isn't it too slow for a real-time application, and you are right; to some extent. The most compute-heavy operations, like predictions or image processing, are being performed by PyTorch and OpenCV both of which use c behind the scene to implement these operations, therefore it won't make much difference if we use c or python for our use case here.


The Complete Self-Driving Car Course - Applied Deep Learning

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Free Coupon Discount - The Complete Self-Driving Car Course - Applied Deep Learning, Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python Created by Rayan Slim English [Auto], French [Auto] Preview this Udemy Course - GET COUPON CODE Self-driving cars have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.


What Waabi's launch means for the self-driving car industry

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It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon. Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company's financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community. What makes Waabi qualified for such support?


What Waabi's launch means for the self-driving car industry

#artificialintelligence

It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon. Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company's financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community. What makes Waabi qualified for such support?


A foolproof guide to image manipulation in Python with OpenCV

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Whether you want to build a complex deep learning model for a self-driving car, a live face recognition program, or making your image processing software for your graduate project, you will have to learn OpenCV along the way. OpenCV is a huge image and video processing library designed to work with many languages such as python, C/C, Java, and more. It is the foundation for many of the applications you know that deal with image processing. Getting started with OpenCV can be challenging, primarily if you rely on its official documentation, which is known for being cumbersome and hard to understand. Attend the tech festival of the year and get your super early bird ticket now!


Council Post: Five Industries Reaping The Benefits Of Artificial Intelligence

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AI (artificial intelligence) offers numerous opportunities to increase your business's value. If implemented in the right way, it can help you optimize your operations, improve overall sales and utilize your manpower in more important tasks. That's why AI is being used in many industries across the globe, such as health care, finance, manufacturing and more. Moreover, it also has multiple branches for different needs, such as deep learning, image processing, natural language processing, neural networks, machine learning, etc. Here are some of the top examples of different enterprises implementing successful AI projects. Hopefully these use cases inspire you to find ways to implement this tool in your own industry.


Data Preprocessing for Machine Learning

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If you are like a measurable amount of programmers out there then you may be interested in Machine Learning(ML). More specifically you might be inspired by hearing or reading about stories of success in the ML industry: from self-driving cars, to robots that can learn to walk and jump, to automations that help advertise better, to trading robots that can make decisions without discretionary input from an investor. The future is certainly an exciting and somewhat scary journey we are going through together. Recently, the advancement of technologies and open source / private projects have brought the ML field into the hands of the people by decentralizing much of the advanced power behind this industry. If you have heard of names like TensorFlow, PyTorch, Sklearn, XGBoost, or Keras, to name a few, you know what I am talking about.


Archeologists have taught computers to sort ancient pottery fragments

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Archeologists at Northern Arizona University (NAU) have taught computers to sort pottery fragments by design and style to assist in classification and reconstruction. A team at NAU's department of anthropology used a form of machine learning known as Convolutional Neural Networks (CNNs) to create a computerized method that emulates the thought processes of the human mind when it analyzes visual information to rapidly and consistently sort thousands of pottery designs into categories. CNNs are commonly used in computer image recognition processes like comparing X-rays to medical conditions, matching images in search engines and in self-driving cars. "Now, using digital photographs of pottery, computers can accomplish what used to involve hundreds of hours of tedious, painstaking and eye-straining work by archaeologists who physically sorted pieces of broken pottery into groups, in a fraction of the time and with greater consistency," said study author Leszek Pawlowicz, in a release. The research results are due to be published in the June edition of the Journal of Archeological Science.


Deep Learning for Autonomous Driving: A Breakthrough in Urban Navigation

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Autonomous vehicle' is a buzzword that's been circulating in recent decades. However, the development of such a vehicle has posed a significant challenge for automotive manufacturers. This article describes how deep learning autonomous driving and navigation can help to turn the concept into a long-awaited reality. The low-touch economy in a post-pandemic world is driving the introduction of autonomous technologies that can satisfy our need for contactless interactions. Whether it's self-driving vehicles delivering groceries or medicines or robo-taxis driving us to our desired destinations, there's never been a bigger demand for autonomy.