从 Quora 的 187 个问题中学习机器学习和NLP
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Quora 已经变成了一个获取重要资源的有效途径。许多的顶尖研究人员都会积极的在现场回答问题。
以下是一些在 Quora 上有关 AI 的主题。如果你已经在 Quora 上面注册了账号，你可以订阅这些主题。
- 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)
虽然 Quora 有许多主题的常见问题（FAQ）页面（比如，这是一个机器学习的 FAQ），但是这些 FAQ 都是非常不全面的，或者不够精致。在这篇文章中，我试图做一个更加全面的有关机器学习和NLP问题的FAQ。
Quora 中的问答没有那么有结构性，很多对问题的回答都是非常不尽如人意。所以，我们尽量去整理一些好的问题和一些相关的好的问答。
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?