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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-self-driving-cars/
课程评论：没有评论
课程名称：机器学习与自动驾驶汽车：Python引导训练营 课程概述：您是否对机器学习或自动驾驶汽车（如特斯拉）感兴趣？那么这个课程就是为您设计的！该课程由一位专业数据科学家、自动驾驶车辆专家编制，旨在以简单的方式分享知识，帮助您理解自动驾驶汽车的工作原理。每个主题分为三个层次进行呈现： 1. **入门级**：介绍主题及其基本直觉。 2. **实践级**：通过动手实践的讲解来学习。 3. **深入级/可选**：深入数学，全面理解主题。 课程中使用的工具包括： - **Python**：一种极具 versatility 的编程语言，适用于从网站到深度神经网络的各种应用。 - **Python库**：如matplotlib、OpenCV、numpy、scikit-learn、keras等，这些库使Python的应用可能性无限。 - **Webots**：一个功能强大的模拟器，免费且开源，能够提供多种模拟场景（如自动驾驶汽车、无人机、四足机器人、机械臂、生产线等）。 **适合人群**： 该课程适合所有水平的学习者，不需要任何先前的知识，包含一节教授如何编程的Python部分。数学/逻辑方面的基础高中水平即可理解课程内容。 **课程内容包括**： - **可选的Python部分**：教授Python编程及使用基本库的方法。 - **计算机视觉**：教导计算机如何“看”，并介绍神经网络的关键概念。 - **机器学习**：入门，关键概念，以及交通标志分类。 - **碰撞避免**：了解如何使用雷达和激光雷达传感器进行自动驾驶汽车的碰撞避免和路径规划，并帮助区分特斯拉与其他汽车制造商的不同之处。 - **深度学习**：结合之前在计算机视觉、机器学习和碰撞避免部分所学的概念，引入神经网络和行为克隆。 - **控制理论**：控制系统连接所有工程领域的纽带，初步了解其与神经网络的关联。 **讲师背景**： 讲师拥有8年以上行业经验，并在自动驾驶摩托车、船只和汽车领域工作过，拥有机器人和计算机视觉硕士学位，始终关注高效学习，并运用了在课程中学到的各种技术。 这个课程将帮助您全面了解机器学习与自动驾驶汽车的基本原理和实际应用！
Interested in Machine Learning or Self-Driving Cars (i.e. Tesla)? Then this course is for you!This course has been designed by a professional Data Scientist, expert in Autonomous Vehicles, with the goal of sharing my knowledge and help you understand how Self-Driving Cars work in a simple way.Each topic is presented at three levels:Introduction [Beginner]: the topic will be presented, initial intuition about itHands-On [Intermediate]: practical lectures where we will learn by doingDeep dive [Expert/Optional]: going deep into the maths to fully understand the topicWhat tools will we use in the course?Python: probably the most versatile programming language in the world, from websites to Deep Neural Networks, all can be done in PythonPython libraries: matplotlib, OpenCV, numpy, scikit-learn, keras,.(those libraries make the possibilities of Python limitless)Webots: a very powerful simulator, which free and open source but can provide a wide range of simulation scenarios (Self-Driving Cars, drones, quadrupeds, robotic arms, production lines,...)Who this course is for?All-levels: there is no previous knowledge required, there is a section that will teach you how to program in PythonMaths/logic: High-school level is enough to understand everything!Sections:[Optional] Python sections: How to program in python, and how to use essential librariesComputer Vision: teaches a computer how to see, and introduces key concepts for Neural NetworksMachine Learning: introduction, key concepts, and road sign classificationCollision Avoidance: so far we have used cameras, in this section we understand how radar and lidar sensors are used for self-driving cars, use them for collision avoidance, path planningHelp us understand the difference between Tesla and other car manufacturers, because Tesla doesn't use radar sensorsDeep learning: we will use all the concepts that we have seen before in CV, in ML and CA, neural networks introduction, Behavioural CloningControl Theory: control systems is the glue that stitches all engineering fields togetherIf you are mainly interested in ML, you can only listen to the introduction for this section, but you should know that the initial Neural Networks were heavily influenced by CTWho am I, and why am I qualified to talk about Self-driving cars?Worked in self-driving motorbikes, boats and carsSome of the biggest companies in the worldOver 8 years experience in the industry and a master in Robotic & CVAlways been interested in efficient learning, and used all the techniques that I've learned in this course
