從 Quora 的 187 個問題中學習機器學習和 NLP

Quora 已經變成了一個獲取重要資源的有效途徑。許多的頂尖研究人員都會積極的在現場回答問題。本文將收集一些比較好的文章資源，提供給大家學習。

本場 Chat 只有文章，沒有交流。

Quora 已經變成了一個獲取重要資源的有效途徑。許多的頂尖研究人員都會積極的在現場回答問題。

以下是一些在 Quora 上有關 AI 的主題。如果你已經在 Quora 上面註冊了賬號，你可以訂閱這些主題。

• Computer-Science (5.6M followers)
• Machine-Learning (1.1M followers)
• Artificial-Intelligence (635K followers)
• Deep-Learning (167K followers)
• Natural-Language-Processing (155K followers)
• Classification-machine-learning (119K followers)
• Artificial-General-Intelligence (82K followers)
• Convolutional-Neural-Networks-CNNs (25K followers)
• Computational-Linguistics (23K followers)
• Recurrent-Neural-Networks (17.4K followers)

雖然 Quora 有許多主題的常見問題（FAQ）頁面（比如，這是一個機器學習的 FAQ），但是這些 FAQ 都是非常不全面的，或者不夠精緻。在這篇文章中，我試圖做一個更加全面的有關機器學習和NLP問題的FAQ。

Quora 中的問答沒有那麼有結構性，很多對問題的回答都是非常不盡如人意。所以，我們儘量去整理一些好的問題和一些相關的好的問答。

Machine Learning

• How do I learn machine learning?
• What is machine learning?
• What is machine learning in layman’s terms?
• What is the difference between statistics and machine learning?
• What machine learning theory do I need to know in order to be a successful machine learning practitioner?
• What are the top 10 data mining or machine learning algorithms?
• What exactly is a “hyperparameter” in machine learning terminology?
• How does a machine-learning engineer decide which neural network architecture (feed-forward, recurrent or CNN) to use to solve their problem?
• What’s the difference between gradient descent and stochastic gradient descent?
• How can I avoid overfitting?
• What is the role of the activation function in a neural network?
• What is the difference between a cost function and a loss function in machine learning?
• What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?
• What is regularization in machine learning?
• What is the difference between L1 and L2 regularization?
• What is the difference between Dropout and Batch Normalization?
• What is an intuitive explanation for PCA?
• When and where do we use SVD?
• What is an intuitive explanation of the relation between PCA and SVD?
• Which is your favorite Machine Learning algorithm?
• What is the future of machine learning?
• What are the Top 10 problems in Machine Learning for 2017?

Classification

• What are the advantages of different classification algorithms?
• What are the advantages of using a decision tree for classification?
• What are the disadvantages of using a decision tree for classification?
• What are the advantages of logistic regression over decision trees?
• How does randomization in a random forest work?
• Which algorithm is better for non linear classification?
• What is the difference between Linear SVMs and Logistic Regression?
• How can l apply an SVM for categorical data?
• How do I select SVM kernels?
• How is root mean square error (RMSE) and classification related?
• Why is “naive Bayes” naive?

Regression

• How would linear regression be described and explained in layman’s terms?
• What is an intuitive explanation of a multivariate regression?
• Why is logistic regression considered a linear model?
• Logistic Regression: Why sigmoid function?
• When should we use logistic regression and Neural Network?
• How are linear regression and gradient descent related?
• What is the intuition behind SoftMax function?
• What is softmax regression?

Supervised Learning

• What is supervised learning?
• What does “supervision” exactly mean in the context of supervised machine learning?
• Why isn’t supervised machine learning more automated?
• What are the advantages and disadvantages of a supervised learning machine?
• What are the main supervised machine learning methods?
• What is the difference between supervised and unsupervised learning algorithms?

Reinforcement Learning

• How do I learn reinforcement learning?
• What’s the best way and what are the best resources to start learning about deep reinforcement learning?
• What is the difference between supervised learning and reinforcement learning?
• How does one learn a reward function in Reinforcement Learning (RL)?
• What is the Future of Deep Reinforcement Learning (DL + RL)?
• Is it possible to use reinforcement learning to solve any supervised or unsupervised problem?
• What are some practical applications of reinforcement learning?
• What is the difference between Q-learning and R-learning?
• In what way can Q-learning and neural networks work together?

Unsupervised Learning

• Why is unsupervised learning important?
• What is the future of deep unsupervised learning?
• What are some issues with Unsupervised Learning?
• What is unsupervised learning with example?
• Why could generative models help with unsupervised learning?
• What are some recent and potentially upcoming breakthroughs in unsupervised learning?
• Can neural networks be used to solve unsupervised learning problems?
• What is the state of the art of Unsupervised Learning, and is human-likeUnsupervised Learning possible in the near future?
• Why is reinforcement learning not considered unsupervised learning?

Deep Learning

• What is deep learning?
• What is the difference between deep learning and usual machine learning?
• As a beginner, how should I study deep learning?
• What are the best resources to learn about deep learning?
• What is the difference between deep learning and usual machine learning?
• What’s the most effective way to get started with Deep Learning?
• Is there something that Deep Learning will never be able to learn?
• What are the limits of deep learning?
• What is next for deep learning?
• What other ML areas can replace deep learning in the future?
• What is the best back propagation (deep learning) presentation for dummies?
• Does anyone ever use a softmax layer mid-neural network rather than at the end?
• What’s the difference between backpropagation and backpropagation through time?
• What is the best visual explanation for the back propagation algorithm for neural networks?
• What is the practical usage of batch normalization in neural networks?
• In layman’s terms, what is batch normalisation, what does it do, and why does it work so well?
• Does using Batch Normalization reduce the capacity of a deep neural network?
• What is an intuitive explanation of Deep Residual Networks?
• Is fine tuning a pre-trained model equivalent to transfer learning?
• What would be a practical use case for Generative models?
• Is cross-validation heavily used in Deep Learning or is it too expensive to be used?
• What is the importance of Deep Residual Networks?
• Where is Sparsity important in Deep Learning?
• Why are Autoencoders considered a failure?
• In deep learning, why don’t we use the whole training set to compute the gradient?

Convolutional Neural Networks

• What is a convolutional neural network?
• What is an intuitive explanation for convolution?
• How do convolutional neural networks work?
• How long will it take for me to go from machine learning basics to convolutional neural network?
• Why are convolutional neural networks well-suited for image classification problems?
• Is a pooling layer necessary in CNN? Can it be replaced by convolution?
• How can the filters used in Convolutional Neural Networks be optimized or reduced in size?
• Is the number of hidden layers in a convolutional neural network dependent on size of data set?
• How can convolutional neural networks be used for non-image data?
• Can I use Convolution neural network to classify small number of data, 668 images?
• Why are CNNs better at classification than RNNs?
• What is the difference between a convolutional neural network and a multilayer perceptron?
• What makes convolutional neural network architectures different?
• What’s an intuitive explanation of 1x1 convolution in ConvNets?
• Why does the convolutional neural network have higher accuracy, precision, and recall rather than other methods like SVM, KNN, and Random Forest?
• How can I train Convolutional Neural Networks (CNN) with non symmetric images of different sizes?
• How can l choose the dimensions of my convolutional filters and pooling in convolutional neural network?
• Why would increasing the amount of training data decrease the performance of a convolutional neural network?
• How can l explain that applying max-pooling/subsampling in CNN doesn’t cause information loss?
• How do Convolutional Neural Networks develop more complex features?
• Why don’t they use activation functions in some CNNs for some last convolution layers?
• What methods are used to increase the inference speed of convolutional neural networks?
• What is the usefulness of batch normalization in very deep convolutional neural network?
• Why do we use fully connected layer at the end of a CNN instead of convolution layers?
• What may be the cause of this training loss curve for a convolution neural network?
• The convolutional neural network I’m trying to train is settling at a particular training loss value and a training accuracy just after a few epochs. What can be the possible reasons?
• Why do we use shared weights in the convolutional layers of CNN?
• What are the advantages of Fully Convolutional Networks over CNNs?
• How is Fully Convolutional Network (FCN) different from the original Convolutional Neural Network (CNN)?

Recurrent Neural Networks

• Artificial Intelligence: What is an intuitive explanation for recurrent neural networks?
• How are RNNs storing ‘memory’?
• What are encoder-decoder models in recurrent neural networks?
• Why do Recurrent Neural Networks (RNN) combine the input and hidden state together and not seperately?
• What is an intuitive explanation of LSTMs and GRUs?
• Are GRU (Gated Recurrent Unit) a special case of LSTM?
• How many time-steps can LSTM RNNs remember inputs for?
• How does attention model work using LSTM?
• How do RNNs differ from Markov Chains?
• For modelling sequences, what are the pros and cons of using Gated Recurrent Units in place of LSTMs?
• What is exactly the attention mechanism introduced to RNN (recurrent neural network)? It would be nice if you could make it easy to understand!
• Is there any intuitive or simple explanation for how attention works in the deep learning model of an LSTM, GRU, or neural network?
• Why is it a problem to have exploding gradients in a neural net (especially in an RNN)?
• For a sequence-to-sequence model in RNN, does the input have to contain only sequences or can it accept contextual information as well?
• Can “generative adversarial networks” be used in sequential data in recurrent neural networks? How effective would they be?
• What is the difference between states and outputs in LSTM?
• What is the advantage of combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)?
• Which is better for text classification: CNN or RNN?
• How are recurrent neural networks different from convolutional neural networks?

Natural Language Processing

• As a beginner in Natural Language processing, from where should I start?
• What is the relation between sentiment analysis, natural language processing and machine learning?
• What is the current state of the art in natural language processing?
• What is the state of the art in natural language understanding?
• Which publications would you recommend reading for someone interested in natural language processing?
• What are the basics of natural language processing?
• Could you please explain the choice constraints of the pros/cons while choosing Word2Vec, GloVe or any other thought vectors you have used?
• How do you explain NLP to a layman?
• How do I explain NLP, text mining, and their difference in layman’s terms?
• What is the relationship between N-gram and Bag-of-words in natural language processing?
• Is deep learning suitable for NLP problems like parsing or machine translation?
• What is a simple explanation of a language model?
• What is the definition of word embedding (word representation)?
• How is Computational Linguistics different from Natural Language Processing?
• Natural Language Processing: What is a useful method to generate vocabulary for large corpus of data?
• How do I learn Natural Language Processing?
• Natural Language Processing: What are good algorithms related to sentiment analysis?
• What makes natural language processing difficult?
• What are the ten most popular algorithms in natural language processing?
• What is the most interesting new work in deep learning for NLP in 2017?
• How is word2vec different from the RNN encoder decoder?
• How does word2vec work?
• What’s the difference between word vectors, word representations and vector embeddings?
• What are some interesting Word2Vec results?
• How do I measure the semantic similarity between two documents?
• What is the state of the art in word sense disambiguation?
• What is the main difference between word2vec and fastText?
• In layman terms, how would you explain the Skip-Gram word embedding model in natural language processing (NLP)?
• In layman’s terms, how would you explain the continuous bag of words (CBOW) word embedding technique in natural language processing (NLP)?
• What is natural language processing pipeline?
• What are the available APIs for NLP (Natural Language Processing)?
• How does perplexity function in natural language processing?
• How is deep learning used in sentiment analysis?

Generative Adversarial Networks

• Was Jürgen Schmidhuber right when he claimed credit for GANs at NIPS 2016?
• Can “generative adversarial networks” be used in sequential data in recurrent neural networks? How effective would they be?
• What are the (existing or future) use cases where using Generative Adversarial Network is particularly interesting?
• Can autoencoders be considered as generative models?
• Why are two separate neural networks used in Generative Adversarial Networks?
• What is the advantage of generative adversarial networks compared with other generative models?
• What are some exciting future applications of Generative Adversarial Networks?
• Do you have any ideas on how to get GANs to work with text?
• In what way are Adversarial Networks related or different to Adversarial Training?
• What are the pros and cons of using generative adversarial networks (a type of neural network)?
• Can Generative Adversarial networks use multi-class labels?

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