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使用 TensorFlow 进行理论机器学习和高级机器学习
在开始学习下面的学习资料之前，请确保：
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完成我们提供的使用 TensorFlow 进行机器学习的基础知识这门课程，或者掌握同等知识
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具有软件开发经验，尤其是 Python 开发经验
本课程对于想要实现以下目标的人员来说是一个起点：
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加强对机器学习的理解
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开始理解论文中的观点并通过 TensorFlow 实现论文中的方法
在继续学习之前，您应该已经掌握了关于机器学习工作原理的背景知识，或者已经看完入门课程使用 TensorFlow 进行机器学习的基础知识中的学习资料。以下内容旨在指导学习者学习更多理论知识和高级机器学习内容。您会发现很多课程都采用了 TensorFlow，不过，这些知识也可以套用到其他机器学习框架中。
要进一步了解机器学习，您应该具有 Python 编程经验，以及微积分、线性代数、概率和统计学方面的背景知识。为了帮助您加深对机器学习的了解，我们列出了大学推荐的许多资源和课程，以及一些教材。
第 1 步：复习数学概念
线性代数的本质
由 3Blue1Brown 发布
3blue1brown 发布的一系列以独特视觉角度解说的短视频，其中讲解了如何从几何方面理解矩阵、行列式、本征函数/本征值等内容。
微积分的本质
由 3Blue1Brown 发布
3blue1brown 发布的一系列以独特视觉角度解说的短视频，其中讲解了微积分的基础知识，旨在让您深入理解基本定理，而不只是了解方程的原理。
麻省理工学院课程 18.06：线性代数
此入门课程由麻省理工学院发布，内容涵盖矩阵理论和线性代数。此课程重点介绍了在其他学科中很有用的概念，包括方程组、向量空间、行列式、特征值、相似度和正定矩阵。
第 2 步：通过这些课程和图书加深对深度学习的理解
学习这些课程：
DeepLearning.AI
《深度学习》专项课程
通过学习这 5 门课程，您将了解深度学习的基础知识，了解如何构建神经网络，以及如何成功完成机器学习项目并在 AI 领域成就一番事业。在学习过程中，您不仅可以掌握理论知识，还将了解这些理论在行业中的运用情况。
⬆ 另请 ⬇
阅读这些图书：
深度学习
由 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 共同编著
这本《深度学习》教材旨在帮助学生和从业者进入一般的机器学习领域，尤其是深度学习领域。
神经网络与深度学习
由 Michael Nielsen 编著
本书提供了有关神经网络的理论背景。本书并未用到 TensorFlow，但对于有兴趣深入学习的学生来说具有重要的参考价值。
第 3 步：阅读论文并通过 TensorFlow 实现论文中的方法
[null,null,[],[],[],null,["# Theoretical and Advanced Machine Learning\n\n[TensorFlow](/tutorials) › [Resources](/resources/models-datasets) › [Learn ML](/resources/learn-ml) › [Guide](/resources/learn-ml/theoretical-and-advanced-machine-learning) › \n\nTheoretical and advanced machine learning with TensorFlow\n=========================================================\n\nBefore starting on the learning materials below, be sure to:\n\n1. Complete our curriculum [Basics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning), or have equivalent knowledge\n\n2. Have software development experience, particularly in Python\n\nThis curriculum is a starting point for people who would like to:\n\n1. Improve their understanding of ML\n\n2. Begin understanding and implementing papers with TensorFlow\n\nYou should already have background knowledge of how ML works or completed the learning materials in the beginner curriculum [Basics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning) before continuing. The below content is intended to guide learners to more theoretical and advanced machine learning content. You will see that many of the resources use TensorFlow, however, the knowledge is transferable to other ML frameworks.\n\nTo further your understanding of ML, you should have Python programming experience as well as a background in calculus, linear algebra, probability, and statistics. To help you deepen your ML knowledge, we have listed a number of recommended resources and courses from universities, as well as a couple of textbooks. \n\nStep 1: Refresh your understanding of math concepts\n---------------------------------------------------\n\nML is a math heavy discipline. If you plan to modify ML models, or build new ones from scratch, familiarity with the underlying math concepts is important. You don't have to learn all the math upfront, but instead you can look up concepts you are unfamiliar with as you come across them. If it's been a while since you've taken a math course, try watching the [Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) and the [Essence of calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) playlists from 3blue1brown for a refresher. We recommend that you continue by taking a class from a university, or watching open access lectures from MIT, such as [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) or [Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/). \n[Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) \nby 3Blue1Brown \nA series of short, visual videos from 3blue1brown that explain the geometric understanding of matrices, determinants, eigen-stuffs and more. \nFree [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) \nMath \n[Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) \nby 3Blue1Brown \nA series of short, visual videos from 3blue1brown that explain the fundamentals of calculus in a way that give you a strong understanding of the fundamental theorems, and not just how the equations work. \nFree [View series](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) \nMath \n[MIT 18.06: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) \nThis introductory course from MIT covers matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices. \nFree [View course](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) \nMath \n[MIT 18.01: Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) \nThis introductory calculus course from MIT covers differentiation and integration of functions of one variable, with applications. \nFree [View course](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) \nMath \n\nStep 2: Deepen your understanding of deep learning with these courses and books\n-------------------------------------------------------------------------------\n\nThere is no single course that will teach you everything you need to know about deep learning. One approach that may be helpful is to take a few courses at the same time. Although there will be overlap in the material, having multiple instructors explain concepts in different ways can be helpful, especially for complex topics. Below are several courses we recommend to help get you started. You can explore each of them together, or just choose the ones that feel the most relevant to you.\n\nRemember, the more you learn, and reinforce these concepts through practice, the more adept you will be at building and evaluating your own ML models. \n\n##### Take these courses:\n\n[MIT course 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/) is an introductory course for Deep Learning with TensorFlow from MIT and also a wonderful resource.\n\nAndrew Ng's [Deep Learning Specialization at Coursera](https://www.coursera.org/specializations/deep-learning) also teaches the foundations of deep learning, including convolutional networks, RNNS, LSTMs, and more. This specialization is designed to help you apply deep learning in your work, and to build a career in AI. \n[MIT 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/) \nIn this course from MIT, you will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. \nFree [View course](http://introtodeeplearning.com/) \nCode \nMath \nTheory \nBuild \n\nDeepLearning.AI\n[Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) \nIn five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects and build a career in AI. You will master not only the theory, but also see how it is applied in industry. \n[View course](https://www.coursera.org/specializations/deep-learning) \nCode \nMath \nTheory \nBuild \n\n##### ⬆ And ⬇\nRead these books:\n\nTo complement what you learn in the courses listed above, we recommend that you dive deeper by reading the books below. Each book is available online, and offers supplementary materials to help you practice.\n\nYou can start by reading [Deep Learning: An MIT Press Book](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Deep Learning textbook is an advanced resource intended to help students deepen their understanding. The book is accompanied by [a website](http://www.deeplearningbook.org/), which provides a variety of supplementary materials, including exercises, lecture slides, corrections of mistakes, and other resources to give you hands on practice with the concepts.\n\nYou can also explore Michael Nielsen's online book [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/). This book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more. \n[Deep Learning](https://www.deeplearningbook.org/) \nby Ian Goodfellow, Yoshua Bengio, and Aaron Courville \nThis Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general, and deep learning in particular. \nFree [View book](https://www.deeplearningbook.org/) \nMath \nTheory \nBuild \n[Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) \nby Michael Nielsen \nThis book provides a theoretical background on neural networks. It does not use TensorFlow, but is a great reference for students interested in learning more. \nFree [View book](http://neuralnetworksanddeeplearning.com/) \nCode \nMath \nTheory \nBuild \n\nStep 3: Read and implement papers with TensorFlow\n-------------------------------------------------\n\nAt this point, we recommend reading papers and trying the [advanced tutorials](/tutorials) on our website, which contain implementations of a few well known publications. The best way to learn an advanced application, [machine translation](/tutorials/text/transformer), or [image captioning](/tutorials/text/image_captioning), is to read the paper linked from the tutorial. As you work through it, find the relevant sections of the code, and use them to help solidify your understanding. \n[Previous\nBasics of machine learning with TensorFlow](/resources/learn-ml/basics-of-machine-learning) [Next\nSpecialization: Basics of TensorFlow for Javascript development](/resources/learn-ml/basics-of-tensorflow-for-js-development) \n\nLearn, develop and build with TensorFlow\n----------------------------------------\n\n[Get started](/learn)"]]
