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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-computer-vision/
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课程名称：深度学习：高级计算机视觉（GANs、SSD及更多！） 课程概述： 您是否曾经想知道OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等AI技术是如何运作的？在这门课程中，您将学习这些突破性应用的基础。这是一门令人兴奋的课程，展示了深度学习在近年来的迅速发展。 课程内容包括：从您熟悉的基础卷积神经网络（CNN）架构出发，过渡到现代的新型架构，如VGG、ResNet和Inception。我们将这些技术应用于血细胞图像，创建出比人类医生更优越的医疗专家系统，暗示未来的医生可能是机器人。 您还将学习如何将CNN转变为对象检测系统，不仅能分类图像，还可以定位图像中的每个对象并预测其标签。这项任务是自动驾驶汽车的基本需求（必须实时检测汽车、行人、自行车、交通信号灯等）。 课程将介绍一种先进的算法SSD，它比以往的算法更快、更准确。同时，我们还将探讨另一种流行的计算机视觉任务——神经风格迁移，快速生成结合了内容图像与风格图像的新图像。 此外，您将了解到著名的GAN（生成对抗网络）架构，学习如何使用神经网络生成顶尖的照片级真实图像。我们实现的对象定位是构建完整对象检测系统的关键第一步。 课程的核心主题之一是我们从CNN本身转向涉及CNN的系统。课程重点关注高层构建块，几乎不涉及数学，并且会使用Keras进行大部分编程，避免复杂的底层代码。 本课程的独特之处在于，每行代码都有详细解释，您可以随时通过电子邮件咨询。您将学会从零开始实现机器学习算法，而不是仅仅将数据插入库。 建议先修课程：需要会使用一些库（优选Python）构建、训练和使用CNN，理解卷积和神经网络的基础理论知识，并具备基本的Python编程技能，尤其是在数据科学和Numpy Stack方面的能力。 如果您对这些先进的CNN应用感到兴奋，我期待在课堂上见到您！
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This is one of the most exciting courses I've done and it really shows how fast and how far deep learning has come over the years.When I first started my deep learning series, I didn't ever consider that I'd make two courses on convolutional neural networks.I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Let me give you a quick rundown of what this course is all about:We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)We're going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.In this course, you'll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)We'll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.Another very popular computer vision task that makes use of CNNs is called neural style transfer.This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.I hope you're excited to learn about these advanced applications of CNNs, I'll see you in class!AWESOME FACTS:One of the major themes of this course is that we're moving away from the CNN itself, to systems involving CNNs.Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you."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 courses where you will learn how to implement machine learning algorithms from scratchOther 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...Suggested Prerequisites:Know how to build, train, and use a CNN using some library (preferably in Python)Understand basic theoretical concepts behind convolution and neural networksDecent Python coding skills, preferably in data science and the Numpy StackWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
