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掌控学习之路

要成为机器学习领域的专家,您首先需要在以下四个学习领域打下坚实的基础:编码、数学、机器学习理论以及如何从头到尾构建您自己的机器学习项目。

从 TensorFlow 的精选课程着手,提升您在这四个方面的技能,或者通过浏览下面的资源库选择您自己的学习路线。

机器学习培训的四个领域

在开启您的学习之旅之前,请务必先了解如何学习机器学习知识。我们将学习流程划分为四个知识领域,每个领域均提供了机器学习的基础知识部分。为帮助您顺利完成学习之旅,我们列出了一些图书、视频和在线课程,不仅有助于您提升能力,还可以让您为在自己的项目中使用机器学习做好准备。我们的指导课程旨在扩大您的知识面,因此可以先从这里着手,或者通过浏览我们的资源库选择您自己的学习路径。

  • 编码技能:构建机器学习模型不仅要了解机器学习的概念,还需要编码,以便管理数据、调整参数以及解析测试和优化模型所需的结果。

  • 数学和统计学:机器学习是一门数学密集型学科,因此,如果您打算修改机器学习模型或从头开始构建新模型,那么熟悉基础数学概念对于该过程至关重要。

  • 机器学习理论:了解机器学习理论的基础知识将为您打下基础,并且可在出现问题时帮助您排查问题。

  • Build your own projects: Getting hands on experience with ML is the best way to put your knowledge to the test, so don't be afraid to dive in early with a simple colab or tutorial to get some practice.

TensorFlow 课程

我们提供的指导课程包含推荐的课程、图书和视频,选择一门课程开启学习之旅吧。

针对新手
TensorFlow 的机器学习基础知识

Learn the basics of ML with this collection of books and online courses. You will be introduced to ML and guided through deep learning using TensorFlow 2.0. Then you will have the opportunity to practice what you learn with beginner tutorials.

针对中级水平人员和专家
使用 TensorFlow 进行理论机器学习和高级机器学习

了解机器学习的基础知识之后,您便可以深入研究神经网络和深度学习的理论知识,并加强对基础数学概念的理解,从而将您的能力提升至一个新水平。

针对新手
TensorFlow for JavaScript development

了解使用 JavaScript 开发机器学习模型的基础知识,以及如何直接在浏览器中部署模型。该系列课程将向您简要介绍深度学习,以及如何通过实践练习开始使用 TensorFlow.js。

教育资源

选择您自己的学习路径,并浏览 TensorFlow 团队推荐的图书、课程、视频和练习,以了解机器学习的基础知识。

图书

阅读是理解机器学习和深度学习基础知识的最佳方式之一。图书可以为您提供必要的理论知识,帮助您以后更加快速地学习新概念。

AI and Machine Learning for Coders
by Laurence Moroney

这本入门书籍从代码的角度介绍了如何实现最常见的机器学习场景,例如计算机视觉、自然语言处理 (NLP),以及网络、移动、云端和嵌入式运行时的序列建模。

Deep Learning with Python
by Francois Chollet

本书介绍了关于使用 Keras 进行深度学习的实用操作说明。

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
by Aurélien Géron

本书采用了具体示例和两个可用于生产的 Python 框架(Scikit-Learn 和 TensorFlow),并且理论知识篇幅不多,可帮助您直观地理解构建智能系统用到的概念和工具。

Deep Learning
by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

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。

Neural Networks and Deep Learning
by Michael Nielsen

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。

Learning TensorFlow.js
by Gant Laborde

A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.

Deep Learning with JavaScript
by Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet

本书由 TensorFlow 库的主要作者编著,提供了关于在浏览器中或 Node 上使用 JavaScript 构建深度学习应用的精彩用例和深入说明。

Online courses

学习由多个部分组成的在线课程是学习机器学习基础概念的绝佳方式。很多课程不仅生动形象地讲解了专业知识,还提供了在您的工作或个人项目中直接运用机器学习所需的工具。

Intro to TensorFlow for AI, ML, and Deep Learning

Developed in collaboration with the TensorFlow team, this course is part of the TensorFlow Developer Specialization and will teach you best practices for using TensorFlow.

Intro to TensorFlow for Deep Learning

在这门由 TensorFlow 团队和 Udacity 联合制作的在线课程中,您将了解如何使用 TensorFlow 构建深度学习应用。

TensorFlow Developer Specialization

在这个由 TensorFlow 开发者讲授且由 4 门课程组成的专项课程中,您将了解开发者在 TensorFlow 中构建由 AI 提供支持且可扩容的算法时使用的工具和软件。

Machine Learning Crash Course

有关 TensorFlow API 的机器学习速成课程是面向志向远大的机器学习从业者的自学指南。其中包含一系列视频讲座课程、实际案例研究和实践练习。

MIT 6.S191: Introduction to Deep Learning

通过学习麻省理工学院推出的这门课程,您将掌握深度学习算法的基础知识,并获得在 TensorFlow 中构建神经网络的实践经验。

Deep Learning Specialization

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。

TensorFlow: Data and Deployment Specialization

您已经学习了如何构建和训练模型。现在请学习这个由 4 门课程组成的专项系列课程,了解如何在各种场景下部署模型,以及如何使用数据更有效地训练模型。

TensorFlow: Advanced Techniques Specialization

This specialization is for software and ML engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

数学概念

要更深入地学习机器学习知识,这些资源可以帮助您理解提升至更高水平所需的基础数学概念。

A friendly introduction to linear algebra for ML

A bird's-eye view of linear algebra for machine learning. Never taken linear algebra or know a little about the basics, and want to get a feel for how it's used in ML? Then this video is for you.

Mathematics for Machine Learning Specialization

Coursera 发布的这门在线专项课程旨在弥合数学与机器学习之间的缺口,让您快速掌握基础数学知识以建立直观的理解,并将其与机器学习和数据科学联系起来。

Deep learning
by 3Blue1Brown

3blue1brown 专门通过以视觉为主导的方法讲解数学知识。在此视频系列课程中,您将通过数学概念了解神经网络的基础知识及其运行原理。

Essence of Linear Algebra
by 3Blue1Brown

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。

Essence of Calculus
by 3Blue1Brown

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。

MIT 18.06: Linear Algebra

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。

MIT 18.01: Single Variable Calculus

本课程是麻省理工学院发布的微积分入门课程,其中介绍了一元函数的微分和积分及其应用。

Seeing Theory
by Daniel Kunin, Jingru Guo, Tyler Dae Devlin, Daniel Xiang

概率和统计学的直观介绍。

An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani

本书以通俗易懂的方式概述了统计学习领域,统计学习是一种重要的工具包,可帮助理解训练机器学习模型所需的庞大而复杂的数据集。

TensorFlow resources

We've gathered our favorite resources to help you get started with TensorFlow libraries and frameworks specific to your needs. Jump to our sections for TensorFlow.js, TensorFlow Lite, and TensorFlow Extended.


You can also browse the official TensorFlow guide and tutorials for the latest examples and colabs.

ML Zero to Hero

This introductory series is for people who know how to code, but don't necessarily know machine learning. See a a basic 'Hello World' example of building an ML model, and learn how to build an image classifier by convolutional neural network.

TensorFlow from the Ground Up

This ML Tech Talk is designed for those that know the basics of Machine Learning but need an overview on the fundamentals of TensorFlow (tensors, variables, and gradients without using high level APIs).

Intro to Deep Learning

This ML Tech Talk includes representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow.

Coding TensorFlow

In this series, the TensorFlow Team looks at various parts of TensorFlow from a coding perspective, with videos for use of TensorFlow's high-level APIs, natural language processing, neural structured learning, and more.

Spotting and solving everyday problems with machine learning

Learn to spot the most common ML use cases including analyzing multimedia, building smart search, transforming data, and how to quickly build them into your app with user-friendly tools.

For Javascript

Explore the latest resources at TensorFlow.js.

Learning TensorFlow.js
by Gant Laborde

A hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js.

开始通过 TensorFlow 使用 TensorFlow.js

该专项课程由 3 个部分组成,探讨了如何使用 TensorFlow.js 训练和执行机器学习模型,并向您展示了如何使用 JavaScript 创建直接在浏览器中执行的机器学习模型。

TensorFlow.js: Intelligence and Learning Series
by The Coding Train

这一系列视频是关于机器学习和构建神经网络的大型系列课程的一部分,重点介绍了 TensorFlow.js、核心 API 以及如何使用 JavaScript 库训练和部署机器学习模型。

针对移动设备和 IoT 设备

Explore the latest resources at TensorFlow Lite.

On-Device Machine Learning

Learn how to build your first on-device ML app through learning pathways that provide step-by-step guides for common use cases including audio classification, visual product search, and more.

Introduction to TensorFlow Lite

Learn how to deploy deep learning models on mobile and embedded devices with TensorFlow Lite in this course, developed by the TensorFlow team and Udacity as a practical approach to model deployment for software developers.

针对生产

Explore the latest resources at TFX.

ML engineering for production ML deployments with TFX

Get a hands-on look at how to put together a production pipeline system with TFX. We'll quickly cover everything from data acquisition, model building, through to deployment and management.

Building Machine Learning Pipelines
by Hannes Hapke, Catherine Nelson

This book walks you through the steps of automating an ML pipeline using the TensorFlow ecosystem. The machine learning examples in this book are based on TensorFlow and Keras, but the core concepts can be applied to any framework.

Machine Learning Engineering for Production (MLOps) Specialization

Expand your production engineering capabilities in this four-course specialization. Learn how to conceptualize, build, and maintain integrated systems that continuously operate in production.

ML Pipelines on Google Cloud

This advanced course covers TFX components, pipeline orchestration and automation, and how to manage ML metadata with Google Cloud.

以人为本的 AI

在设计机器学习模型或构建 AI 驱动的应用时,请务必考虑与产品进行互动的用户,以及在这些 AI 系统中纳入公平性、可解释性、隐私性和安全性的最佳方式。

Responsible AI practices

了解如何利用 TensorFlow 在机器学习工作流程中落实 Responsible AI 做法。

人与 AI 指南

Google 发布的这份指南将帮助您构建以人为本的 AI 产品。通过参考本指南,您在构建由 AI 驱动的应用时能够避开常见错误、设计出色的体验并注重以人为本。

“机器学习公平性简介”单元

Google MLCC 中的这一单元(时长 1 小时)向学习者介绍了可能会在训练数据中显现出来的不同类型的人为偏差,以及识别和评估其影响的策略。