Autonomous Vehicles: Instructional Materials


Data Science: Machine Learning algorithms in Matlab

@machinelearnbot

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


The Biggest Hurdle to Building AI Apps, And How to Fix It!

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Building apps for cloud is a thing of the past. Yes, the new wave is Artificial Intelligence (AI) and machine learning. Unfortunately, AI apps that happen to learn from the experiences are lagging way behind when compared to self-driving cars and their evolvement. Since the whole concept of AI revolves around making life easier for the user, the same rule of thumb applies to artificial intelligence apps. Developers are perplexed about what exactly goes into developing such apps.


Data Science: Supervised Machine Learning in Python

@machinelearnbot

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Building Advanced OpenCV3 Projects with Python Udemy

@machinelearnbot

OpenCV is a native cross-platform C library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python. This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset.


Here's how you can master AI & machine learning for just $39

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Ideal for the aspiring developer, this collection boasts more than 30 hours of training on the technology that powers today's AI breakthroughs. Make your way through each of the four courses, and you'll foster new, practical knowledge in machine learning algorithms, AI applications, and more. Artificial Intelligence & Machine Learning Training -- This course offers a solid introduction to the current and potential applications of AI. You'll learn the basic ideas and techniques used in the design of intelligent computer systems, and, once you have your feet wet, you'll advance to statistical and decision-theoretic modeling paradigms, deep learning, and a host of other advanced topics. Introduction to Machine Learning -- In just two hours, this course will walk you through machine learning, the technology that powers self-driving cars, search engines, and more of today's AI breakthroughs.


Learn with Google AI: Google brings free machine learning course, here are the key points

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Whether you you need guidance on learning to code or you are a seasoned machine learning practitioner, Google is here to help. The search engine giant has come up with a new course -- 'Learn with Google AI' -- that acts as a practical introduction to machine learning to all users for free. Machine learning (ML) is a branch of artificial intelligence (AI) and pertains to a computer's ability to execute certain tasks on its own without being programmed by a human being. Some examples of ML include self-driving cars, speech recognition, language translators, etc. Google's new machine learning crash course is designed to provide a fast-paced self-study guide for aspiring machine learning practitioners using high-level TensorFlow (TF) APIs. It features a series of video lessons with lectures from ML experts, real-world case studies and hands-on practice exercises to help users learning about key ML algorithms and frameworks.


Teaching Autonomous Driving Using a Modular and Integrated Approach

arXiv.org Artificial Intelligence

Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.


MIT 6.S094: Deep Learning for Self-Driving Cars

@machinelearnbot

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.


Overview of Udacity Artificial Intelligence Engineer Nanodegree, Term 1

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After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.


Machine Learning For Absolute Beginners Udemy

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If you've ever wanted Jetsons to be real, well we aren't that far off from a future like that. If you've ever chatted with automated robots, then you've definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it's reach and making our devices smarter.