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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unsupervised-deep-learning-in-python/
课程评论：没有评论
课程名称：无监督深度学习与Python 课程概述：你是否想过像OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何工作的？在本课程中，你将学习这些开创性应用的基础。这门课程是我深度学习、数据科学和机器学习系列中的下一步。我之前已经讲解过许多深度学习的课程，并刚刚发布了一门关于无监督学习的课程，其中涵盖了聚类和密度估计。那么，当你将这两者结合起来，会得到什么呢？无监督深度学习！ 在这门课程中，我们将从一些基础知识开始，包括主成分分析（PCA），以及一种流行的非线性降维技术——t-SNE（t分布随机邻居嵌入）。接着，我们将探索一种特殊的无监督神经网络类型——自编码器。在解释自编码器的工作原理后，我将展示如何将多个自编码器连接在一起，形成一个深层自编码器堆栈，从而提高监督深度神经网络的性能。自编码器类似于PCA的非线性形式。 最后，我们将研究限制玻尔兹曼机（RBM），这也是一种流行的无监督神经网络，可以像自编码器一样用于预训练你的监督深度神经网络。我将展示一种训练限制玻尔兹曼机的有趣方法，称为吉布斯采样，这是一种马尔可夫链Monte Carlo的特例。我会演示尽管这种方法仅仅是粗略的近似，但它仍能降低其他成本函数，例如自编码器所用的函数。这种方法也被称为对比散度（Contrastive Divergence，CD-k）。和物理系统一样，我们定义了一个称为自由能的概念，并试图最小化这个量。 最后，我们将把所有这些概念结合起来，我将通过可视化展示当你在自编码器和RBM学习的特征上使用PCA和t-SNE时发生的变化，尽管没有标签，结果仍然表明找到了模式。 课程中的所有材料都是免费的。由于这门课程是深度学习系列的第四门课程，我假设你已经具备微积分、线性代数和Python编程的基础。你需要为本课程安装Numpy、Theano和Tensorflow，这些是你数据分析工具箱中的基本项目。 如果你对深度学习感兴趣，并希望了解现代深度学习发展，尤其是如何利用无监督神经网络解释深度学习系统中可以被自动和分层学习的特征，这门课程适合你。本课程专注于“如何构建和理解”，而不仅仅是“如何使用”。任何人都能在阅读一些文档后，在15分钟内学会使用API，但这不是目标。我们要通过实验来“亲自看到”，而不是“记住事实”。你将学习如何可视化模型内部发生的事情。如果你想更深入地了解机器学习模型，这门课程适合你。 “如果你无法实现它，你就不了解它。”或者正如著名物理学家理查德·费曼所说：“我无法创造的东西，我就不理解。” 我的课程是唯一可以让你学习如何从零开始实现机器学习算法的课程。其他课程可能教你如何将数据插入库中，但你真的需要帮助来处理三行代码吗？做完10个数据集的重复工作后，你会意识到你没有学到10件事，你只学到了一件事，且重复相同的三行代码10次。 建议的先修知识：微积分、线性代数、概率、Python编码（if/else、循环、列表、字典、集合）、Numpy编码（矩阵和向量操作、加载CSV文件）、能在Theano或Tensorflow中编写前馈神经网络的能力。 你应该按什么顺序学习我的课程呢？请查看每门课程中的“机器学习与人工智能先修路线图”讲座（包括免费的Numpy课程的常见问题解答中提供）。
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 course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).Next, we'll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I'll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.Last, we'll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I'll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I'll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD-k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity.Finally, we'll bring all these concepts together and I'll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we'll see that even without labels the results suggest that a pattern has been found.All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and Python coding. You'll want to install Numpy, Theano, and Tensorflow for this course. These are essential items in your data analytics toolbox.If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is 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:calculuslinear algebraprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV filecan write a feedforward neural network in Theano or TensorflowWHAT 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)
