第1章

【Google Cloud API】 https://cloud.google.com/natural-language/

【Microsoft Azure的文本分析API】 https://azure.microsoft.com/is-is/services/concepting-services/text-analytics/

【Google Translate】 https://cloud.google.com/translate/

【AllenNLP的语义角色标记】 https://demo.allennlp.org/semantic-role-labeling

【Quepy】 http://quepy.machinalis.com/

【AllenNLP的机器阅读理解】 http://demo.allennlp.org/machine-comprehension

【AllenNLP的文字蕴含】 http://demo.allennlp.org/textual-entailment

【AllenNLP的指代消解】 http://demo.allennlp.org/coreference-resolution

【Bing API】 https://azure.microsoft.com/zh-CN/services/cognitive-services/bing-web-search-api/

【聊天机器人的范式】 https://aws.amazon.com/lex/details/

【Google文本转语音】 https://cloud.google.com/text-to-speech/

【Google语音转文本】 https://cloud.google.com/speech-to-text/

【AllenNLP的成分语法分析】 http://demo.allennlp.org/constituency-parsing

第3章

【CUDA下载页面】 https://developer.nvidia.com/cuda-downloads

【cuDNN下载页面】 https://developer.nvidia.com/cudnn

第5章


数据集名称:源网址

IMDb movie Dataset:http://ai.stanford.edu/~amaas/data/sentiment/

Twiiter Sentiment Analysis Dataset:http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/

YouTube Spam Collection Dataset:https://archive.ics.uci.edu/ml/datasets/YouTube+Spam+Collection

News Aggregator Dataset:https://archive.ics.uci.edu/ml/datasets/News+Aggregator

Yelp reviews:https://www.yelp.com/dataset

Amazon reviews:http://jmcauley.ucsd.edu/data/amazon/

Reuters Corpora:http://trec.nist.gov/data/reuters/reuters.html


【第5章完整的公开数据集列表可从UCI库(https://archive.ics.uci.edu/ml/datasets.html)和Kaggle(https://archive.ics.uci.edu/ml/datasets.html)获取。】

【Latent Dirichlet Allocation】 https://dl.acm.org/citation.cfm?id=944937

【Large-scale Multi-label Text Classification – Revisiting Neural Networks】 https://arxiv.org/abs/1312.5419

【Bag of Tricks for Efficient Text Classification】 https://arxiv.org/abs/1607.01759

【Bag of Tricks for Efficient Text Classification】 https://arxiv.org/abs/1607.01759

【Deep Learning for Extreme Multi-label Text Classification】 https://dl.acm.org/citation.cfm?id=3080834

【Hierarchical Attention Networks for Document Classfication】 http://www.cs.cmu.edu/~./hovy/papers/16HLT-hierarchical-attention-networks.pdf

【GitHub项目richliao/textClassifier】 https://github.com/richliao/textClassifier

【GitHub项目ematvey/hierarchical-attention-networks】https://github.com/ematvey/hierarchical-attention-networks

第8章

【Linux内核档案】 https://www.kernel.org/

【Abstractive Text Summarization Using Sequence-To-Sequence RNNs and Beyond】 https://arxiv.org/abs/1602.06023

【Get to the Point: Summarization with Pointer-Generator Networks】 https://arxiv.org/abs/1704.04368

【QA学术数据集】


数据集名称:URL

bAbI text understanding tasks:https://research.fb.com/downloads/babi/

SQuAD: Stanford Question Answering Dataset:https://stanford-qa.com/

VQA: Visual Question Answering Dataset:http://www.visualqa.org/

AI2 Reasoning Challenge:http://data.allenai.org/arc/


【Memory Networks】 http://arxiv.org/abs/1410.3916

第9章

【End-to-End Memory Networks】 http://arxiv.org/abs/1503.08895

【Learning End-to-End Goal-Oriented Dialog】 http://arxiv.org/abs/1605.07683

【bAbI dialog数据集下载】 https://research.fb.com/downloads/babi/


论文标题:ArXiv URL

Dynamic Memory Networks (DMNs) and Dynamic Coattention Networks (DCNs):https://arxiv.org/abs/1506.07285https://arxiv.org/abs/1711.00106

Neural Turing Machines (NTMs) Differentiable Neural Computer (DNC):https://arxiv.org/abs/1410.5401https://www.nature.com/articles/nature20101

Seq2seq Memory Network:https://arxiv.org/pdf/1702.01932.pdf

Recurrent Entity Networks:https://arxiv.org/pdf/1612.03969.pdf


第10章

【Google Translate服务】 https://translate.google.com/

【TED演讲的翻译】 https://wit3.fbk.eu/mt.php?release=2015-0

第11章

【Jakobovski的免费数字音频数据集】 https://github.com/Jakobovski/free-spoken-digit-dataset/tree/master/recording

【librosa】 https://github.com/librosa/librosa

【DeepSpeech模型原始论文】 https://arxiv.org/abs/1412.5567

【语言数据协会语音录音下载】 https://www.kaggle.com/nltkdata/timitcorpus

【DeepSpeech在大型数据集上的实现】 https://github.com/mozilla/DeepSpeech

【Listen, Attend, and Spell: A Neural Network for Large Vocabulary Conversational Speech Recognition】 https://a.google/research/pubs/pub44926

【DeepSpeech模型的PyTorch实现】 https://github.com/XenderLiu/ListenAttend-and-Spell-Pytorc

第12章

【WaveNet: A Generative Model for Raw Audio】 https://arxiv.org/abs/1609.03499

【Tacotron】 https://arxiv.org/abs/1703.10135

【dropout】 http://jmlr.org/apers/v15/srivastava14a.html

【Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift】 https://arxiv.org/abs/1502.03167

【残差连接】 https://arxiv.org/abs/1512.03385

【高速公路网络】 https://arxiv.org/abs/1505.00387

【Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation】 https://arxiv.org/abs/1609.08144

【Griffin-Lim】 https://ieeexplore.ieee.org/document/1164317/

【Adam优化器】 https://arxiv.org/abs/1412.6980

【LJ语音数据集】 https://keithito.com/LJ-SpeechDataset/

第13章

【TensorFlow Serving安装指导】 https://www.tensorflow.org/serving/setup

【Google Cloud Platform】 https://cloud.google.com/

【CoreML2】 https://developer.apple.com/machine-learning/