深度学习 免费课程

by David Venturi

大卫·文图里(David Venturi)

深入学习深度学习，提供15项免费在线课程 (Dive into Deep Learning with 15 free online courses)

Every day brings new headlines for how deep learning is changing the world around us. A few examples:

每天都有新的标题报道深度学习如何改变我们周围的世界。 一些例子：

• Deep learning algorithm diagnoses skin cancer as well as seasoned dermatologists

  深度学习算法可诊断皮肤癌以及经验丰富的皮肤科医生

• Amazon Go: How Deep Learning and AI will change Retailing

  Amazon Go：深度学习和人工智能将如何改变零售

• Deep Learning Makes Driverless Cars Better at Spotting Pedestrians

  深度学习使无人驾驶汽车更适合发现行人

Want to see what the fuss is all about? Looking to master the technical content to advance your career or start your own company? I explored the open source project Class Central and found 31 online courses (15 of which are completely free) that cover everything from the basics of deep learning to the most cutting edge research today.

想看看大惊小怪吗？ 想要掌握技术内容以发展您的职业或创办自己的公司？ 我浏览了开源项目Class Central ，发现了31个在线课程(其中15个是完全免费的)，涵盖了从深度学习的基础到当今最前沿的研究的所有内容。

Before we start, you may be asking yourself: “What exactly is deep learning?” Here’s a succinct description:

在开始之前，您可能会问自己：“深度学习到底是什么？” 这是一个简洁的描述：

“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”

“深度学习是机器学习的一个子领域，涉及受大脑结构和功能启发的算法，称为人工神经网络。”

— Jason Brownlee from Machine Learning Mastery

—来自机器学习精通的 Jason Brownlee

Without further ado…

无需再费周折…

在线深度学习课程 (Online Deep Learning Courses)

Creative Applications of Deep Learning with TensorFlowvia Kadenze★★★★★ (14 ratings)

通过Kadenze 使用TensorFlow 进行 深度学习的创造性应用 ★★★★★( 14个评分 )

We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. Free and paid options are available.

我们涵盖了深度学习的基本组成部分，它的含义，工作方式，并开发了构建各种算法(例如深度卷积网络，变分自动编码器，生成对抗网络和递归神经网络)所需的代码。 本课程的主要重点不仅在于了解如何构建这些算法的必要组件，还在于如何将其应用于探索创造性的应用程序。 提供免费和付费选项。

Prominent review (by Christopher Kelly): “I have an undergraduate degree in computer science … I’ve spent a ton of time on Khan Academy and Coursera and I’m blown away by the quality and professionalism of the content of this course. Highly recommended!”

杰出的评论( 克里斯托弗·凯利 ( Christopher Kelly ))： “我拥有计算机科学的本科学位……我在可汗学院和Coursera上度过了很多时间，并且对这门课程的内容的质量和专业性感到震惊。 强烈推荐！”

Neural Networks for Machine LearningUniversity of Toronto via Coursera★★★★★ (18 ratings)

多伦多大学 机器学习的神经网络， 通过Coursera ★★★★★( 18个评分 )

Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Free and paid options are available.

了解有关人工神经网络及其在机器学习中如何用于语音和对象识别，图像分割，建模语言和人体运动等的知识。我们将重点介绍获得所需的基本算法和实用技巧他们工作得很好。 提供免费和付费选项。

Prominent review (by Bobby Brady): “This is one of those chance in a lifetime courses you have to get to learn from the greats. Geoffrey Hinton was one of the most important and influential researchers to work on artificial intelligence and neural nets back in the 80’s. Currently he is working with Google in their AI/deep learning initiatives.”

杰出的评论(由Bobby Brady撰写 )：“ 这是一生中必须向伟大的人学习的机会之一。 杰弗里·欣顿(Geoffrey Hinton)是80年代从事人工智能和神经网络研究的最重要和最有影响力的研究人员之一。 目前，他正在与Google合作开展AI /深度学习计划。”

Practical Deep Learning For Coders, Part 1fast.ai★★★★☆ (3 ratings)

面向程序员的 实用深度学习，第1部分 fast.ai ★★★★☆( 3评分 )

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one — learning how to get a GPU server online suitable for deep learning — and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. Free.

这项为期7周的课程是为至少具有一年编码经验并且对高中数学有一定记忆的人设计的。 您将从第一步开始-学习如何在线获得适用于深度学习的GPU服务器-并一直创建最先进的，高度实用的计算机视觉模型，自然语言处理和推荐系统。 自由。

Prominent review (by Anonymous): “This is really a hidden gem in a field that rapidly growing. Jeremy Howard does an excellent job of both walking through the basics and presenting state of the art results. I was surprised time and again when not only was he presenting material developed within the last year, but even within the week the course was running … You practice on real life data through Kaggle competitions. I would strongly recommend this course to anyone looking to go from zero real world experience to competing with experts in the field.”

杰出评论( 匿名人士 )： “这确实是一个Swift发展的领域中的隐藏宝石。 杰里米·霍华德(Jeremy Howard)出色地完成了基础知识并展示了最新技术成果。 我一次又一次地感到惊讶，他不仅介绍了去年开发的材料，甚至还介绍了课程运行的一周之内……您通过Kaggle竞赛练习现实数据。 我强烈建议向希望从零现实世界经验转变为与该领域的专家竞争的任何人使用此课程。”

6.S191: Introduction to Deep LearningMassachusetts Institute of Technology (MIT)★★★★☆ (1 rating)

6.S191： 麻省理工学院(MIT) 深度学习简介 ★★★★☆( 1评分 )

A week-long intro to deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Free.

为期一周的深度学习方法入门，涉及机器翻译，图像识别，游戏，图像生成等方面的应用。 包含TensorFlow中的实验室和同行集体讨论以及讲座的协作课程。 自由。

6.S094: Deep Learning for Self-Driving CarsMassachusetts Institute of Technology (MIT)★★★★☆ (1 rating)

6.S094：自动驾驶汽车的深度学习 麻省理工学院(MIT) ★★★★☆( 1评分 )

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. Free.

本课程通过构建自动驾驶汽车的应用主题介绍深度学习的实践。 它对初学者开放，并且是为机器学习的新手而设计的，但它也可以为寻求深度学习方法及其应用的实用概述的本领域的高级研究人员提供帮助。 自由。

Deep LearningGoogle via Udacity★★☆☆☆ (20 ratings)

通过Udacity 深度学习 Google ★★☆☆☆( 20评分 )

In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods. Free.

在本课程中，您将清楚了解深度学习的动机，并设计可从复杂和/或大规模数据集中学习的智能系统。 您将学习解决曾经被认为极具挑战性的新问题类别，并在使用深度学习方法轻松解决这些相同问题的同时，更好地理解人类智能的复杂性。 自由。

Deep Learning for Natural Language ProcessingUniversity of Oxford

牛津大学 自然语言处理深度学习

This is an applied course focusing on recent advances in analyzing and generating speech and text using recurrent neural networks. The mathematical definitions of the relevant machine learning models are introduced and their associated optimization algorithms are derived.

这是一门应用课程，着重于使用递归神经网络分析和生成语音和文本的最新进展。 介绍了相关机器学习模型的数学定义，并推导了它们的相关优化算法。

The course, which is free, is lead by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.

该课程是免费的，由Phil Blunsom主持 ，并与DeepMind自然语言研究小组合作提供。

CS224n: Natural Language Processing with Deep LearningStanford University

CS224n： 斯坦福大学 深度学习的自然语言处理

The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. Through lectures (note: Winter 2017 videos now posted) and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems. Free.

该课程全面介绍了应用于NLP的深度学习前沿研究。 在模型方面，我们将涵盖词向量表示，基于窗口的神经网络，递归神经网络，长期短期记忆模型，递归神经网络，卷积神经网络以及一些涉及内存组件的最新模型。 通过讲座 ( 请注意：现已发布2017年冬季视频 )和编程任务，学生将学习使神经网络解决实际问题的必要工程技巧。 自由。

CS231n: Convolutional Neural Networks for Visual RecognitionStanford University

CS231n：用于视觉识别的卷积神经网络 斯坦福大学

This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Free.

本课程深入研究了深度学习架构的细节，重点是学习这些任务的端到端模型，尤其是图像分类。 在为期10周的课程中，学生将学习实现，训练和调试自己的神经网络，并获得对计算机视觉前沿研究的详细了解。 最后的任务将涉及训练数百万个参数的卷积神经网络，并将其应用于最大的图像分类数据集(ImageNet)。 我们将重点讲授如何设置图像识别问题，学习算法(例如，反向传播)，用于训练和微调网络的实用工程技巧，并通过动手作业和最终课程项目指导学生。 自由。

Machine LearningNando de Freitas/University of British Columbia

机器学习 Nando de Freitas /不列颠哥伦比亚大学

This course focuses on the exciting field of deep learning. By drawing inspiration from neuroscience and statistics, it introduces the basic background on neural networks, back propagation, Boltzmann machines, autoencoders, convolutional neural networks and recurrent neural networks. It illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines. Free.

本课程侧重于深度学习的令人兴奋的领域。 通过借鉴神经科学和统计学的启发，它介绍了神经网络，反向传播，玻尔兹曼机器，自动编码器，卷积神经网络和递归神经网络的基本背景。 它说明了深度学习如何影响我们对智能的理解并有助于智能机器的实际设计。 自由。

Deep Learning Summer School 2015 and 2016Various organizers (including Yoshua Bengio and Yann LeCun) via Independent

2015年 和2016年 深度学习暑期学校 通过独立组织 ， 各种组织者(包括Yoshua Bengio和Yann LeCun)

Deep Learning Summer School is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

深度学习暑期班针对的是研究生，工业工程师和研究人员，他们已经掌握了一些机器学习的基础知识(并且可能但不一定是深度学习的知识)，并且希望进一步了解这一Swift发展的研究领域。

It isn’t organized like a traditional online course, but its organizers (including deep learning luminaries such as Bengio and LeCun) and the lecturers they attract make this series a gold mine for deep learning content. It is free.

它的组织方式不同于传统的在线课程，但其组织者(包括Bengio和LeCun等深度学习专家)及其吸引的讲师使该系列成为深度学习内容的金矿。 这是免费的。

Online Course on Neural NetworksHugo Larochelle/Université de Sherbrooke

神经网络在线课程 Hugo Larochelle /舍布鲁克大学

“Welcome to my online course on neural networks! I’ve put this course together while teaching an in-class version of it at the Université de Sherbrooke. This is a graduate-level course, which covers basic neural networks as well as more advanced topics.” Free.

“欢迎来到我的神经网络在线课程！ 我在舍布鲁克大学(Universitéde Sherbrooke)讲授课程的同时，将这门课程放在一起。 这是一门研究生课程，涵盖基础神经网络以及更高级的主题。” 自由。

Learn TensorFlow and deep learning, without a Ph.D.Google

无需博士学位即可学习TensorFlow和深度学习。 谷歌

This three-hour course (video and slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Free.

这个为时三小时的课程(视频和幻灯片)为开发人员提供了深入学习基础知识的快速入门，其中一些TensorFlow也很便宜。 自由。

Deep Learning 101Big Data University

深度学习101 大数据大学

The further one dives into the ocean, the more unfamiliar the territory can become. Deep learning, at the surface might appear to share similarities. This course is designed to get you hooked on the nets and coders all while keeping the school together. Free.

越深入海洋，领土就会变得越陌生。 从表面上看，深度学习似乎具有相似之处。 本课程旨在使您与网络和编码员紧密相连，同时使学校保持在一起。 自由。

Deep Learning with TensorFlowBig Data University

TensorFlow 大数据大学的 深度学习

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you’ll use Google’s library to apply deep learning to different data types in order to solve real world problems. Free.

世界上大多数数据都是未标记和未结构化的。 浅层神经网络无法轻松捕获图像，声音和文本数据等相关结构。 深度网络能够发现此类数据中的隐藏结构。 在本TensorFlow课程中，您将使用Google的库将深度学习应用于不同的数据类型，以解决现实世界中的问题。 自由。

Deep Learning in PythonDataCamp

Python DataCamp中的 深度学习

In this course, you’ll gain hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python. Partially free.

在本课程中，您将获得有关如何使用神经网络和深度学习与Keras 2.0进行动手实践的知识，Keras 2.0是Python深度学习的最新库的最新版本。 部分免费。

The following courses, sorted by rating, are all hosted on Udemy. Prices vary depending on Udemy discounts, which are frequent. You are often able to purchase access for as little as $10.

以下课程按评分排序，均在Udemy上托管。 价格根据Udemy的折扣而有所不同，这些折扣经常出现。 您通常可以以低至10美元的价格购买访问权限。

Please note that the offerings from Lazy Programmer Inc. come with a suggested order as per the “Useful Course Ordering” guideline in their course descriptions.

请注意，Lazy Programmer Inc.的产品在其课程说明中按照“有用的课程订购”指南提供了建议的订购。

Deep Learning A-Z™: Hands-On Artificial Neural NetworksKirill Eremenko and the SuperDataScience Team via Udemy4.7 stars (388 ratings)

深度学习AZ™：动手人工神经网络 Kirill Eremenko和SuperDataScience团队(通过Udemy) 4.7星级(388个评分)

Learn to create deep learning algorithms in Python from two machine learning and data science experts. Templates included. This course is taught by the same instructor that teaches my top recommendation for intro to data science courses.

向两位机器学习和数据科学专家学习使用Python创建深度学习算法。 包含模板。 本课程由同一位老师讲授，该老师教给我有关数据科学入门课程的最高推荐 。

I ranked every Intro to Data Science course on the internet, based on thousands of data pointsmedium.freecodecamp.com

我根据数千个数据点 medium.freecodecamp.com 对互联网上的每门数据科学入门课程进行了 排名。

Zero to Deep Learning™: Mastering KerasData Weekends, Jose Portilla, Francesco Mosconi4.8 stars (23 ratings)

零深度学习™：掌握Keras 数据周末，何塞·波蒂利亚，弗朗切斯科·莫斯科尼 4.8星级(23评分)

Understand and build Deep Learning models for images, text, sound and more using Python and Keras.

使用Python和Keras了解并构建用于图像，文本，声音等的深度学习模型。

Deep Learning Prerequisites: The Numpy Stack in PythonLazy Programmer Inc. via Udemy4.6 stars (1,551 ratings)

深度学习先决条件：Python Lazy Programmer Inc.中 的Numpy堆栈， 通过Udemy 4.6星级(1,551个评分)

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence. Free.

Numpy，Scipy，Pandas和Matplotlib堆栈：为深度学习，机器学习和人工智能做准备。 自由。

Data Science: Deep Learning in PythonLazy Programmer Inc. via Udemy4.6 stars (1,381 ratings)

数据科学： 通过Udemy 在Python Lazy Programmer Inc.中进行 深度学习 4.6星级(1,381评分)

A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.

使用Python和Numpy编写自己的神经网络以及如何在Google的TensorFlow中进行操作的指南。

Deep Learning Prerequisites: Linear Regression in PythonLazy Programmer Inc. via Udemy4.6 stars (751 ratings)

深度学习先决条件：Python Lazy Programmer Inc.中的 线性回归， 通过Udemy 4.6星(751个评分)

Learn linear regression from scratch and build your own working program in Python for data analysis.

从零开始学习线性回归，并在Python中构建自己的工作程序进行数据分析。

Deep Learning Prerequisites: Logistic Regression in PythonLazy Programmer Inc. via Udemy4.6 stars (624 ratings)

深度学习先决条件：Python Lazy Programmer Inc.中的 逻辑回归 通过Udemy 4.6星级(624个评分)

Data science techniques for professionals and students — learn the theory behind logistic regression and code in Python.

适用于专业人士和学生的数据科学技术-学习逻辑回归和Python代码背后的理论。

Deep Learning: Convolutional Neural Networks in PythonLazy Programmer Inc. via Udemy4.6 stars (304 ratings)

深度学习：Python Lazy Programmer Inc.中的 卷积神经网络， 通过Udemy 4.6星(304评分)

This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

本课程全部关于如何使用卷积神经网络将深度学习用于计算机视觉。 这些是图像分类方面的最新技术，它们在诸如MNIST之类的任务中击败了香草深层网络。

Data Science: Practical Deep Learning in Theano + TensorFlowLazy Programmer Inc. via Udemy4.6 stars (293 ratings)

数据科学：Theano + TensorFlow Lazy Programmer Inc.中的 实用深度学习， 通过Udemy 4.6星级(293评分)

Take deep learning to the next level with SGD, Nesterov momentum, RMSprop, Theano, TensorFlow, and using the GPU on AWS.

通过SGD，Nesterov动量，RMSprop，Theano，TensorFlow以及在AWS上使用GPU，将深度学习提升到一个新的水平。

Deep Learning: Recurrent Neural Networks in PythonLazy Programmer Inc. via Udemy4.6 stars (248 ratings)

深度学习：Python Lazy Programmer Inc.中的 递归神经网络， 通过Udemy 4.6星级(248个评分)

GRU, LSTM, and more modern deep learning, machine learning, and data science for sequences.

GRU，LSTM，以及更现代的深度学习，机器学习和序列数据科学。

Natural Language Processing with Deep Learning in PythonLazy Programmer Inc. via Udemy4.6 stars (194 ratings)

通过Udemy 在Python Lazy Programmer Inc.中进行 深度学习的自然语言处理 4.6星级(194评分)

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

有关使用递归网络派生和实现word2vec，GLove，单词嵌入和情感分析的完整指南。

Unsupervised Deep Learning in PythonLazy Programmer Inc. via Udemy4.6 stars (153 ratings)

通过Udemy 在Python Lazy Programmer Inc.中进行的 无监督深度学习 4.6星级(153评分)

Autoencoders and Restricted Boltzmann Machines for Deep Neural Networks in Theano, and t-SNE and PCA.

Theano中的深层神经网络以及t-SNE和PCA的自动编码器和受限玻尔兹曼机。

Unleash Deep Learning: Begin Visually with Caffe and DIGITSRazvan Pistolea via Udemy4.5 stars (36 ratings)

释放深度学习： 通过Udemy通过 Caffe和DIGITS Razvan Pistolea 从视觉上开始 4.5星级(36评分)

An introduction to Deep Learning tools using Caffe and DIGITS where you get to create your own Deep Learning Model.

有关使用Caffe和DIGITS的深度学习工具的简介，您可以在其中创建自己的深度学习模型。

Deep Learning with TensorFlowPackt Publishing via Udemy3.9 stars (96 ratings)

使用TensorFlow Packt Publishing通过Udemy进行 深度学习 3.9星级(96评分)

Channel the power of deep learning with Google’s TensorFlow!

利用Google的TensorFlow传递深度学习的力量！

Deep Learning with PythonPackt Publishing via Udemy3.4 stars (31 ratings)

通过Udemy通过 Python Packt Publishing进行 深度学习 3.4星级(31评分)

Dive into the future of data science and implement intelligent systems using deep learning with Python.

深入研究数据科学的未来，并使用Python进行深度学习来实现智能系统。

Though it is more of a program than a singular online course, below you’ll find a Udacity Nanodegree targeting the fundamentals of deep learning.

尽管它不是单单的在线课程，它更像是一个程序，但在下面您会发现针对深度学习基础的Udacity纳米学位 。

At time of publishing, the current enrollment period is closed, but you can sign up for the waiting list to be notified when the next enrollment period begins.

发布时，当前的注册期已结束，但是您可以注册以等待下一个注册期开始时的通知。

Deep Learning Foundations NanodegreeSiraj Raval via Udacity

深度学习基金会 通过Udacity提供 纳米学位 Siraj Raval

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress. Together with Siraj Raval, Udacity provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.

人工智能正在以戏剧性和有益的方式改变我们的世界，而深度学习正在推动进步。 Udacity与Siraj Raval一起，通过每周的视频，独家项目以及专家的反馈和评论为您提供了有关这一惊人领域的动态介绍，以教会您这种塑造未来技术的基础。

If you enjoyed reading this, check out some other explorations of Class Central’s dataset:

如果您喜欢阅读本文，请查看Class Central的数据集的其他一些探索：

• Here are 250 Ivy League courses you can take online right now for free

  这里有250个常春藤盟军课程，您可以立即免费在线上报名

• I ranked every Intro to Data Science course on the internet, based on thousands of data points

  我根据数千个数据点对互联网上的每门数据科学入门课程进行了排名

• If you want to learn Data Science, start with one of these programming classes

  如果您想学习数据科学，请从以下编程课程之一开始

• If you want to learn Data Science, take a few of these statistics classes

  如果您想学习数据科学，请参加一些此类统计课程

• The best free online university courses for learning a new world language

  学习新世界语言的最佳免费在线大学课程

• The 50 best free online university courses according to data

  根据数据显示的50种最佳免费在线大学课程

If you know of any courses that I may have missed, please let me know in the responses!

如果您知道我可能错过的任何课程，请在回复中告诉我！

If you found this helpful, click the ? so more people will see it here on Medium.

如果您认为这有帮助，请单击“？”。 因此更多的人会在Medium上看到它。

Originally published on Class Central.

最初发表在Class Central上 。

翻译自: https://www.freecodecamp.org/news/dive-into-deep-learning-with-these-23-online-courses-bf247d289cc0/

深度学习 免费课程