最全面的深度学习自学资源汇总
xietmac
7年前
<p><img src="https://simg.open-open.com/show/1c9e60362a22aeb66364ddead4f6399c.jpg"></p> <p>深度学习作为机器学习的一个分支，是近年来最热门同时也是发展最快的人工智能技术之一，相关学习资源包括免费公开教程和工具都极大丰富，同时这也为学习 <a href="/misc/goto?guid=4959749422471914456" rel="nofollow,noindex">深度学习技术</a> 的IT人才带来选择上的困扰，Yerevann整理的这个深度学习完全指南 ，汇集了目前网络上最优秀的深度学习自学资源，而且会不定期更新，非常值得收藏关注，以下是IT经理网编译整理的指南内容：</p> <p>自学基本要求（数学知识、编程知识）</p> <p>数学知识：学员需要具备普通大学数学知识，例如 《Deep Learning》 一书中若干章节提到的数学概念：</p> <p><a href="/misc/goto?guid=4959749422567329541" rel="nofollow,noindex">Deep Learning第二章：线性代数</a></p> <p><a href="/misc/goto?guid=4959749422666670132" rel="nofollow,noindex">Deep Learning第三章：概率与信息理论</a></p> <p><a href="/misc/goto?guid=4959749422742928411" rel="nofollow,noindex">Deep Learning第四章：数值计算</a></p> <p>编程知识：你需要懂得编程才能开发和测试深度学习模型，我们建议在机器学习领域首选Python。同时也要用到面向科学计算的NumPy/SciPy代码库。资源链接如下（本文出现的星标代表难度等级）：</p> <p><a href="/misc/goto?guid=4959733920883457396" rel="nofollow,noindex">Justin Johnson’s Python / NumPy / SciPy / Matplotlib tutorial for Stanford’s CS231n</a> ★</p> <p><a href="/misc/goto?guid=4959749422867517122" rel="nofollow,noindex">Scipy lecture notes</a> – 涵盖了常用的各种库，介绍也比较详细，还涉及一些深入的技术话题 ★★</p> <p>四大入门教程</p> <p>如果你具备以上自学基本要求技能，我们建议从以下四大入门在线教程中任选一项或多项组合学习（星标为难度等级）：</p> <p><a href="/misc/goto?guid=4959622548948106037" rel="nofollow,noindex"><strong>Hugo Larochelle’s video course</strong> </a> 这是油Tube上很火的一个深度学习视频教程，录制于2013年，但今天看内容并不过时，很详细地阐释了神经网络背后的数学理论。 <a href="/misc/goto?guid=4959553799298993404" rel="nofollow,noindex">幻灯片和相关资料传送门</a> . ★★</p> <p><a href="/misc/goto?guid=4959632483323856632" rel="nofollow,noindex">Stanford’s CS231n</a> (应用于视觉识别的卷积神经网络) 由已经投奔Google的李飞飞教授和 Andrej Karpathy、Justin Johnson共同执教的课程，重点介绍了图像处理，同时也涵盖了深度学习领域的大多数重要概念。 <a href="/misc/goto?guid=4959749423039384465" rel="nofollow,noindex">视频 链接(2016)</a> 、 <a href="/misc/goto?guid=4959648874890857572" rel="nofollow,noindex">讲义传送门</a> ★★</p> <p>Michael Nielsen的在线著作： <a href="/misc/goto?guid=4958862734067045457" rel="nofollow,noindex">Neural networks and deep learning</a> 是目前学习神经网络 <strong>最容易的教材</strong> ，虽然该书并未涵盖所有重要议题，但是包含大量简明易懂的阐释，同时还为一些基础概念提供了实现代码。★</p> <p>Ian Goodfellow、Yoshua Bengio and Aaron Courville共同编著的 <a href="/misc/goto?guid=4958989206881529847" rel="nofollow,noindex">Deep learning</a> 是目前深度学习领域 <strong>最全面的教程</strong> 资源，比其他课程涵盖的范围都要广。 ★★★</p> <p>机器学习基础</p> <p>机器学习是通过数据教计算机做事的科学，同时也是一门艺术，机器学习是计算机科学和数学交汇的一个相对成熟的领域，深度学习只是其中新兴的一小部分，因此，了解机器学习的概念和工具对我们学好深度学习非常重要。以下是机器学习的一些重要学习资源（以下课程介绍部分内容不再翻译）：</p> <p><a href="/misc/goto?guid=4959726303173939947" rel="nofollow,noindex">Visual introduction to machine learning</a> – decision trees ★</p> <p><a href="/misc/goto?guid=4958985450327747017" rel="nofollow,noindex">Andrew Ng’s course on machine learning</a> , the most popular course on Coursera ★★</p> <p>Larochelle’s course doesn’t have separate introductory lectures for general machine learning, but all required concepts are defined and explained whenever needed.</p> <p><a href="https://www.油Tube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA" rel="nofollow,noindex">1. Training and testing the models (kNN) ★★</a></p> <p><a href="https://www.油Tube.com/watch?v=hAeos2TocJ8&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=2" rel="nofollow,noindex">2. Linear classification (SVM) ★★</a></p> <p><a href="https://www.油Tube.com/watch?v=WjY57K9xX4s&index=3&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA" rel="nofollow,noindex">3. Optimization (stochastic gradient descent)</a> ★★</p> <p><a href="/misc/goto?guid=4959749423544192686" rel="nofollow,noindex">5. Machine learning basics</a> ★★★</p> <p><a href="/misc/goto?guid=4959749423624887717" rel="nofollow,noindex">Principal Component Analysis explained visually</a> ★</p> <p><a href="/misc/goto?guid=4959749423709791531" rel="nofollow,noindex">How to Use t-SNE Effectively</a> ★★</p> <p>机器学习的编程学习资料：大多数流行机器学习算法都部署在Scikit-learn 这个Python库中，从头部署算法能够帮我们更好地了解机器学习的工作原理，以下是相关学习资源：</p> <p><a href="/misc/goto?guid=4959749423819948307" rel="nofollow,noindex">Practical Machine Learning Tutorial with Python</a> covers linear regression, k-nearest-neighbors and support vector machines. First it shows how to use them from scikit-learn, then implements the algorithms from scratch. ★</p> <p>Andrew Ng’s course on Coursera has many assignments in Octave language. The same algorithms can be implemented in Python. ★★</p> <p>神经网络基础</p> <p>神经网络是强大的机器学习算法，同时也是深度学习的基础：</p> <p><a href="/misc/goto?guid=4959749423901980297" rel="nofollow,noindex">A Visual and Interactive Guide to the Basics of Neural Networks</a> – shows how simple neural networks can do linear regression ★</p> <p><a href="/misc/goto?guid=4959622548948106037" rel="nofollow,noindex">1. Feedforward neural network</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=5adNQvSlF50&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=7" rel="nofollow,noindex">2. Training neural networks (up to 2.7)</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=GZTvxoSHZIo&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=4" rel="nofollow,noindex">4. Backpropagation</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=GUtlrDbHhJM&index=5&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA" rel="nofollow,noindex">5. Architecture of neural networks</a> ★★</p> <p><a href="/misc/goto?guid=4959749424259913732" rel="nofollow,noindex">1. Using neural nets to recognize handwritten digits</a> ★</p> <p><a href="/misc/goto?guid=4959643110871574774" rel="nofollow,noindex">2. How the backpropagation algorithm works</a> ★</p> <p><a href="/misc/goto?guid=4959749424372215347" rel="nofollow,noindex">4. A visual proof that neural nets can compute any function</a> ★</p> <p><a href="/misc/goto?guid=4959749424458685767" rel="nofollow,noindex">6. Deep feedforward networks</a> ★★★</p> <p><a href="/misc/goto?guid=4959749424543940312" rel="nofollow,noindex">Yes you should understand backprop</a> explains why it is important to implement backpropagation once from scratch ★★</p> <p><a href="/misc/goto?guid=4959643110210761908" rel="nofollow,noindex">Calculus on computational graphs: backpropagation</a> ★★</p> <p><a href="/misc/goto?guid=4958998680454979295" rel="nofollow,noindex">Play with neural networks!</a> ★</p> <p>神经网络实操教程</p> <p><a href="/misc/goto?guid=4959749424682805858" rel="nofollow,noindex">Implementing softmax classifier and a simple neural network in pure Python/NumPy</a> – Jupyter notebook available ★</p> <p>Andrej Karpathy implements backpropagation in Javascript in his <a href="/misc/goto?guid=4958868960757375801" rel="nofollow,noindex">Hacker’s guide to Neural Networks</a> . ★</p> <p><a href="/misc/goto?guid=4959648869992053912" rel="nofollow,noindex">Implementing a neural network from scratch</a> in Python ★</p> <p>改进神经网络学习</p> <p>神经网络的训练可不容易，很多时候机器压根不会学习（underfitting），有时候又“死学”，照本宣科你输入的知识，无法总结归纳出新的数据（overfitting）,解决上述问题的方法有很多，如下是</p> <p>推荐教程：</p> <p><a href="https://www.油Tube.com/watch?v=JfkbyODyujw&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=14" rel="nofollow,noindex">2.8-2.11. Regularization, parameter initialization etc.</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=UcKPdAM8cnI&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=55" rel="nofollow,noindex">7.5. Dropout</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=KaR4lIdI1MQ&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=6" rel="nofollow,noindex">6 (first half). Setting up the data and loss</a> ★★</p> <p><a href="/misc/goto?guid=4959749425087728559" rel="nofollow,noindex">3. Improving the way neural networks learn</a> ★</p> <p><a href="/misc/goto?guid=4959749425167914463" rel="nofollow,noindex">5. Why are deep neural networks hard to train?</a> ★</p> <p><a href="/misc/goto?guid=4959749425255246899" rel="nofollow,noindex">7. Regularization for deep learning</a> ★★★</p> <p><a href="/misc/goto?guid=4959749425336233337" rel="nofollow,noindex">8. Optimization for training deep models</a> ★★★</p> <p><a href="/misc/goto?guid=4959749425426090848" rel="nofollow,noindex">11. Practical methodology</a> ★★★</p> <p><a href="/misc/goto?guid=4959747803054498099" rel="nofollow,noindex">ConvNetJS Trainer demo on MNIST</a> – visualizes the performance of different optimization algorithms ★</p> <p><a href="/misc/goto?guid=4959749425530454704" rel="nofollow,noindex">An overview of gradient descent optimization algorithms</a> ★★★</p> <p><a href="/misc/goto?guid=4958978039547632480" rel="nofollow,noindex">Neural Networks, Manifolds, and Topology</a> ★★★</p> <p>常用的主流框架</p> <p>目前很多 <a href="/misc/goto?guid=4959749425642387405" rel="nofollow,noindex">深度学习算法</a> 都对最新的计算机硬件进行了优化，大多数框架也提供Python接口（Torch除外，需要Lua）。当你了解基本的深度学习算法的部署后，是时候选择一个框架开工了（这部分还可CTOCIO文章： <a href="/misc/goto?guid=4959749425722411297" rel="nofollow,noindex">2016年人气最高的六款开源深度学习工具</a> ）：</p> <p><a href="/misc/goto?guid=4958964178006582027" rel="nofollow,noindex">Theano</a> provides low-level primitives for constructing all kinds of neural networks. It is maintained by <a href="/misc/goto?guid=4959749425833377766" rel="nofollow,noindex">a machine learning group at University of Montreal</a> . See also: <a href="/misc/goto?guid=4959648868176283375" rel="nofollow,noindex">Speeding up your neural network with Theano and the GPU</a> – Jupyter notebook available ★</p> <p><a href="/misc/goto?guid=4958974442727004844" rel="nofollow,noindex">TensorFlow</a> is another low-level framework. Its architecture is similar to Theano. It is maintained by the Google Brain team.</p> <p><a href="/misc/goto?guid=4958837496859528274" rel="nofollow,noindex">Torch</a> is a popular framework that uses Lua language. The main disadvantage is that Lua’s community is not as large as Python’s. Torch is mostly maintained by 非死book and 推ter.</p> <p>There are also higher-level frameworks that run on top of these:</p> <p><a href="/misc/goto?guid=4959749426009231385" rel="nofollow,noindex">Lasagne</a> is a higher level framework built on top of Theano. It provides simple functions to create large networks with few lines of code.</p> <p><a href="/misc/goto?guid=4959741470403953949" rel="nofollow,noindex">Keras</a> is a higher level framework that works on top of either Theano or TensorFlow.</p> <p>如果你有框架选择困难症，可以参考斯坦福课程 <a href="https://www.油Tube.com/watch?v=XgFlBsl0Lq4&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=12" rel="nofollow,noindex">Lecture 12 of Stanford’s CS231n</a> . ★★</p> <p>卷积神经网络</p> <p>卷积神经网络Convolutional networks (CNNs)，是一种特定的神经网络，通过一些聪明的方法大大提高了学习速度和质量。卷积神经网络掀起了计算机视觉的革命，并广泛应用于语音识别和文本归类等领域，以下是</p> <p>推荐教程：</p> <p><a href="https://www.油Tube.com/watch?v=rxKrCa4bg1I&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=69" rel="nofollow,noindex">9. Computer vision (up to 9.9)</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=KaR4lIdI1MQ&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=6" rel="nofollow,noindex">6 (second half). Intro to ConvNets</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=V8JDMkARdfU&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=7" rel="nofollow,noindex">7. Convolutional neural networks</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=2xtx-gk3PqY&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=8" rel="nofollow,noindex">8. Localization and detection</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=N--YsFUyYnE&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=9" rel="nofollow,noindex">9. Visualization, Deep dream, Neural style, Adversarial examples</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=UFnO-ADC-k0&index=13&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA" rel="nofollow,noindex">13. Image segmentation (up to 38:00)</a> includes upconvolutions ★★</p> <p><a href="/misc/goto?guid=4959749426620248892" rel="nofollow,noindex">6. Deep learning</a> ★</p> <p><a href="/misc/goto?guid=4959749426708187384" rel="nofollow,noindex">9. Convolutional networks</a> ★★★</p> <p><a href="/misc/goto?guid=4959749426779230499" rel="nofollow,noindex">Image Kernels explained visually</a> – shows how convolutional filters (also known as image kernels) transform the image ★</p> <p><a href="/misc/goto?guid=4959618217923334440" rel="nofollow,noindex">ConvNetJS MNIST demo</a> – live visualization of a convolutional network right in the browser ★</p> <p><a href="/misc/goto?guid=4958976191612693792" rel="nofollow,noindex">Conv Nets: A Modular Perspective</a> ★★</p> <p><a href="/misc/goto?guid=4958978650027094686" rel="nofollow,noindex">Understanding Convolutions</a> ★★★</p> <p><a href="/misc/goto?guid=4958976191697714407" rel="nofollow,noindex">Understanding Convolutional neural networks for NLP</a> ★★</p> <p>卷积神经网络框架部署和应用</p> <p>所有重要的框架都支持卷积神经网络的部署，通常使用高级函数库编写的代码的可读性要更好一些。</p> <p><a href="/misc/goto?guid=4958968585569702498" rel="nofollow,noindex">Theano: Convolutional Neural Networks (LeNet)</a> ★★</p> <p><a href="/misc/goto?guid=4959749427011796296" rel="nofollow,noindex">Using Lasagne for training Deep Neural Networks</a> ★</p> <p><a href="/misc/goto?guid=4959749427095037724" rel="nofollow,noindex">Detecting diabetic retinopathy in eye images</a> – a blog post by one of the best performers of Diabetic retinopathy detection contest in Kaggle. Includes a good example of data augmentation. ★★</p> <p><a href="/misc/goto?guid=4959749427163122656" rel="nofollow,noindex">Face recognition for right whales using deep learning</a> – the authors used different ConvNets for localization and classification. <a href="/misc/goto?guid=4959749427252291242" rel="nofollow,noindex">Code and models are available</a> . ★★</p> <p><a href="/misc/goto?guid=4959749427332494635" rel="nofollow,noindex">Tensorflow: Convolutional neural networks</a> for image classification on CIFAR-10 dataset ★★</p> <p><a href="/misc/goto?guid=4959749427415756024" rel="nofollow,noindex">Implementing a CNN for text classification in Tensorflow</a> ★★</p> <p><a href="/misc/goto?guid=4959749427506514691" rel="nofollow,noindex">DeepDream implementation in TensorFlow</a> ★★★</p> <p><a href="/misc/goto?guid=4959749427576876707" rel="nofollow,noindex">92.45% on CIFAR-10 in Torch</a> – implements famous VGGNet network with batch normalization layers in Torch ★</p> <p><a href="/misc/goto?guid=4959749427665053190" rel="nofollow,noindex">Training and investigating Residual Nets</a> – Residual networks perform very well on image classification tasks. Two researchers from 非死book and CornellTech implemented these networks in Torch ★★★</p> <p><a href="https://www.油Tube.com/watch?v=ue4RJdI8yRA&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=11" rel="nofollow,noindex">ConvNets in practice</a> – lots of practical tips on using convolutional networks including data augmentation, transfer learning, fast implementations of convolution operation ★★</p> <p>递归神经网络</p> <p>递归神经网络Recurrent entworks（RNNs）被设计用来处理序列数据（例如文本、股票、基因组、传感器等）相关问题，通常应用于语句分类（例如情感分析）和语音识别，也适用于文本生成甚至图像生成。</p> <p>教程如下：</p> <p><a href="/misc/goto?guid=4958976191353379572" rel="nofollow,noindex">The Unreasonable Effectiveness of Recurrent Neural Networks</a> – describes how RNNs can generate text, math papers and C++ code ★</p> <p>Hugo Larochelle’s course doesn’t cover recurrent neural networks (although it covers many topics that RNNs are used for). We suggest watching <a href="/misc/goto?guid=4959663268611667531" rel="nofollow,noindex">Recurrent Neural Nets and LSTMs</a> by Nando de Freitas to fill the gap ★★</p> <p><a href="https://www.油Tube.com/watch?v=cO0a0QYmFm8&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&index=10" rel="nofollow,noindex">10. Recurrent Neural Networks, Image Captioning, LSTM</a> ★★</p> <p><a href="https://youtu.be/UFnO-ADC-k0?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&t=2280" rel="nofollow,noindex">13. Soft attention (starting at 38:00)</a> ★★</p> <p>Michael Nielsen’s book stops at convolutional networks. In the <a href="/misc/goto?guid=4959749428039955736" rel="nofollow,noindex">Other approaches to deep neural nets</a> section there is just a brief review of simple recurrent networks and LSTMs. ★</p> <p><a href="/misc/goto?guid=4959749428123845193" rel="nofollow,noindex">10. Sequence Modeling: Recurrent and Recursive Nets</a> ★★★</p> <p><a href="https://www.油Tube.com/watch?v=nwcJuGuG-0s&index=8&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam" rel="nofollow,noindex">Recurrent neural networks</a> from Stanford’s CS224d (2016) by Richard Socher ★★</p> <p><a href="/misc/goto?guid=4959642620247557394" rel="nofollow,noindex">Understanding LSTM Networks</a> ★★</p> <p>递归神经网络的框架部署与应用</p> <p><a href="/misc/goto?guid=4959749428305827896" rel="nofollow,noindex">Theano: Recurrent Neural Networks with Word Embeddings</a> ★★★</p> <p><a href="/misc/goto?guid=4959749428390403097" rel="nofollow,noindex">Theano: LSTM Networks for Sentiment Analysis</a> ★★★</p> <p><a href="/misc/goto?guid=4959648871325104971" rel="nofollow,noindex">Implementing a RNN with Python, Numpy and Theano</a> ★★</p> <p><a href="/misc/goto?guid=4959749428501353655" rel="nofollow,noindex">Lasagne implementation of Karpathy’s char-rnn</a> ★</p> <p><a href="/misc/goto?guid=4959749428588711749" rel="nofollow,noindex">Combining CNN and RNN for spoken language identification</a> in Lasagne ★</p> <p><a href="/misc/goto?guid=4959749428660781754" rel="nofollow,noindex">Automatic transliteration with LSTM</a> using Lasagne ★</p> <p><a href="/misc/goto?guid=4959749428745556490" rel="nofollow,noindex">Tensorflow: Recurrent Neural Networks</a> for language modeling ★★</p> <p><a href="/misc/goto?guid=4959749428828117139" rel="nofollow,noindex">Recurrent Neural Networks in Tensorflow</a> ★★</p> <p><a href="/misc/goto?guid=4959749428909868766" rel="nofollow,noindex">Understanding and Implementing Deepmind’s DRAW Model</a> ★★★</p> <p><a href="/misc/goto?guid=4959749428993946062" rel="nofollow,noindex">LSTM implementation explained</a> ★★</p> <p><a href="/misc/goto?guid=4959749429067545954" rel="nofollow,noindex">Torch implementation of Karpathy’s char-rnn</a> ★★★</p> <p>Autoencoders</p> <p>Autoencoder是为非监督式学习设计的神经网络，例如当数据没有标记的情况。Autoencoder可以用来进行数据维度消减，以及为其他神经网络进行预训练，以及数据生成等。以下课程资源中，我们还收录了Autoencoder与概率图模型整合的一个autoencoders的变种，其背后的数学机理在下一章“概率图模型”中会介绍。</p> <p>推荐教程：</p> <p><a href="https://www.油Tube.com/watch?v=FzS3tMl4Nsc&t=2s&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=44" rel="nofollow,noindex">6. Autoencoder</a> ★★</p> <p><a href="https://www.油Tube.com/watch?v=z5ZYm_wJ37c&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=56" rel="nofollow,noindex">7.6. Deep autoencoder</a> ★★</p> <p><a href="https://youtu.be/I-i1KBuShCc?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA&t=1949" rel="nofollow,noindex">14. Videos and unsupervised learning (from 32:29)</a> – this video also touches an exciting topic of generative adversarial networks. ★★</p> <p><a href="/misc/goto?guid=4959749429401933164" rel="nofollow,noindex">14. Autoencoders</a> ★★★</p> <p><a href="/misc/goto?guid=4959747802858625275" rel="nofollow,noindex">ConvNetJS Denoising Autoencoder demo</a> ★</p> <p><a href="https://www.油Tube.com/watch?v=P78QYjWh5sM&index=3&list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu" rel="nofollow,noindex">Karol Gregor on Variational Autoencoders and Image Generation</a> ★★</p> <p>Autoencoder的部署</p> <p>大多数autoencoders都非常容易部署，但我们还是建议您从简单的开始尝试。课程资源如下：</p> <p><a href="/misc/goto?guid=4959648874428826760" rel="nofollow,noindex">Theano: Denoising autoencoders</a> ★★</p> <p><a href="/misc/goto?guid=4959749429618067420" rel="nofollow,noindex">Diving Into TensorFlow With Stacked Autoencoders</a> ★★</p> <p><a href="/misc/goto?guid=4959749429696679743" rel="nofollow,noindex">Variational Autoencoder in TensorFlow</a> ★★</p> <p><a href="/misc/goto?guid=4959749429779131347" rel="nofollow,noindex">Training Autoencoders on ImageNet Using Torch 7</a> ★★</p> <p><a href="/misc/goto?guid=4959749429863222361" rel="nofollow,noindex">Building autoencoders in Keras</a> ★</p> <p>概率图模型</p> <p>概率图模型（Probabilistic Graphical model，PGM）是统计学和机器学习交叉分支领域，关于概率图模型的书籍和课程非常多，以下我们收录的资源重点关注概率图模型在深度学习场景中的应用。其中Hugo Larochelles的课程介绍了一些非常著名的模型，而Deep Learning一书有整整四个章节专门介绍，并在最后一章介绍了十几个模型。本领域的学习需要读者掌握大量数学知识：</p> <p><a href="https://www.油Tube.com/watch?v=GF3iSJkgPbA&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=18" rel="nofollow,noindex">3. Conditional Random Fields</a> ★★★</p> <p><a href="https://www.油Tube.com/watch?v=6dpGB60Q1Ts&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=28" rel="nofollow,noindex">4. Training CRFs</a> ★★★</p> <p><a href="https://www.油Tube.com/watch?v=p4Vh_zMw-HQ&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=36" rel="nofollow,noindex">5. Restricted Boltzman machine</a> ★★★</p> <p><a href="https://www.油Tube.com/watch?v=vkb6AWYXZ5I&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=57" rel="nofollow,noindex">7.7-7.9. Deep Belief Networks</a> ★★★</p> <p><a href="https://www.油Tube.com/watch?v=y0SISi_T6s8&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&index=78" rel="nofollow,noindex">9.10. Convolutional RBM</a> ★★★</p> <p><a href="/misc/goto?guid=4959749430345944323" rel="nofollow,noindex">13. Linear Factor Models</a> – first steps towards probabilistic models ★★★</p> <p><a href="/misc/goto?guid=4959749430439502707" rel="nofollow,noindex">16. Structured Probabilistic Models for Deep Learning</a> ★★★</p> <p><a href="/misc/goto?guid=4959749430511923389" rel="nofollow,noindex">17. Monte Carlo Methods</a> ★★★</p> <p><a href="/misc/goto?guid=4959749430598508750" rel="nofollow,noindex">18. Confronting the Partition Function</a> ★★★</p> <p><a href="/misc/goto?guid=4959749430673216357" rel="nofollow,noindex">19. Approximate Inference</a> ★★★</p> <p><a href="/misc/goto?guid=4959749430761633092" rel="nofollow,noindex">20. Deep Generative Models</a> – includes Boltzmann machines (RBM, DBN, …), variational autoencoders, generative adversarial networks, autoregressive models etc. ★★★</p> <p><a href="/misc/goto?guid=4959749430838046374" rel="nofollow,noindex">Generative models</a> – a blog post on variational autoencoders, generative adversarial networks and their improvements by OpenAI. ★★★</p> <p><a href="/misc/goto?guid=4959749430917032492" rel="nofollow,noindex">The Neural Network Zoo</a> attempts to organize lots of architectures using a single scheme. ★★</p> <p>概率图模型的部署</p> <p>高级框架（Lasagne、Keras）不支持概率图模型的部署，但是Theano、Tensorflow和Torch有很多可用的代码。</p> <p><a href="/misc/goto?guid=4959648873852719078" rel="nofollow,noindex">Restricted Boltzmann Machines in Theano</a> ★★★</p> <p><a href="/misc/goto?guid=4959749431037559132" rel="nofollow,noindex">Deep Belief Networks in Theano</a> ★★★</p> <p><a href="/misc/goto?guid=4959749431107515764" rel="nofollow,noindex">Generating Large Images from Latent Vectors</a> – uses a combination of variational autoencoders and generative adversarial networks. ★★★</p> <p><a href="/misc/goto?guid=4959749431192849304" rel="nofollow,noindex">Image Completion with Deep Learning in TensorFlow</a> – another application of generative adversarial networks. ★★★</p> <p><a href="/misc/goto?guid=4959749431266413584" rel="nofollow,noindex">Generating Faces with Torch</a> – Torch implementation of Generative Adversarial Networks ★★</p> <p>精华论文、视频与论坛汇总</p> <p><a href="/misc/goto?guid=4959726303630433405" rel="nofollow,noindex">Deep learning papers reading roadmap</a> 深度学习重要论文的大清单。</p> <p><a href="/misc/goto?guid=4959749431382930380" rel="nofollow,noindex">Arxiv Sanity Preserver</a> 为浏览 arXiv上的论文提供了一个漂亮的界面.</p> <p><a href="/misc/goto?guid=4958826670709786159" rel="nofollow,noindex">Videolectures.net</a> 含有大量关于深度学习的高级议题视频</p> <p><a href="/misc/goto?guid=4959749431493497062" rel="nofollow,noindex">/r/MachineLearning</a> 一个非常活跃的Reddit分支. 几乎所有重要的新论文这里都有讨论。</p> <p> </p> <p>来自：http://dataunion.org/29282.html</p> <p> </p>