Your Journey to NLP Starts Here !
全面拥抱tensorflow2，代码全部修改为tensorflow2.0版本。
二. 经典书目(百度云 提取码：b5qq)
- 概率图入门.
原书地址 - Deep Learning.深度学习必读.
原书地址 - Neural Networks and Deep Learning. 入门必读.
原书地址 - 斯坦福大学《语音与语言处理》第三版：NLP必读.
原书地址
- LSTM(Long Short-term Memory).
地址 - Sequence to Sequence Learning with Neural Networks.
地址 - Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.
地址 - Residual Network(Deep Residual Learning for Image Recognition).
地址 - Dropout(Improving neural networks by preventing co-adaptation of feature detectors).
地址 - Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
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- An overview of gradient descent optimization algorithms.
地址 - Analysis Methods in Neural Language Processing: A Survey.
地址 - Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.
地址 - A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications.
地址 - A Gentle Introduction to Deep Learning for Graphs.
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- EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks.
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- A Neural Probabilistic Language Model.
地址 - word2vec Parameter Learning Explained.
地址 - Language Models are Unsupervised Multitask Learners.
地址 - An Empirical Study of Smoothing Techniques for Language Modeling.
地址 - Efficient Estimation of Word Representations in Vector Space.
地址 - Distributed Representations of Sentences and Documents.
地址 - Enriching Word Vectors with Subword Information(FastText).
地址.解读 - GloVe: Global Vectors for Word Representation.
官网 - ELMo (Deep contextualized word representations).
地址 - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
地址 - Pre-Training with Whole Word Masking for Chinese BERT.
地址 - XLNet: Generalized Autoregressive Pretraining for Language Understanding
地址
- Bag of Tricks for Efficient Text Classification (FastText).
地址 - Convolutional Neural Networks for Sentence Classification.
地址 - Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.
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- A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation.
地址 - SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.
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- Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks.
地址 - Learning Text Similarity with Siamese Recurrent Networks.
地址 - A Deep Architecture for Matching Short Texts.
地址
- A Question-Focused Multi-Factor Attention Network for Question Answering.
地址 - The Design and Implementation of XiaoIce, an Empathetic Social Chatbot.
地址 - A Knowledge-Grounded Neural Conversation Model.
地址 - Neural Generative Question Answering.
地址 - Sequential Matching Network A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots．
地址 - Modeling Multi-turn Conversation with Deep Utterance Aggregation．
地址 - Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network．
地址 - Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes.
地址
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.
地址 - Neural Machine Translation by Jointly Learning to Align and Translate.
地址 - Transformer (Attention Is All You Need).
地址 - Transformer-XL:Attentive Language Models Beyond a Fixed-Length Context.
地址
- Get To The Point: Summarization with Pointer-Generator Networks.
地址 - Deep Recurrent Generative Decoder for Abstractive Text Summarization.
地址
- Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks.
地址 - Neural Relation Extraction with Multi-lingual Attention.
地址 - FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation.
地址 - End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.
地址
- Deep Neural Networks for YouTube Recommendations.
地址 - Behavior Sequence Transformer for E-commerce Recommendation in Alibaba.
地址
- 应聘机器学习工程师？这是你需要知道的12个基础面试问题.
地址 - 如何学习自然语言处理（综合版）.
地址 - The Illustrated Transformer.
地址 - Attention-based-model.
地址 - Modern Deep Learning Techniques Applied to Natural Language Processing.
地址 - Bert解读.
地址 - 难以置信！LSTM和GRU的解析从未如此清晰（动图+视频）。
地址 - 深度学习中优化方法.
地址 - 从语言模型到Seq2Seq：Transformer如戏，全靠Mask.
地址 - Applying word2vec to Recommenders and Advertising.
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- transformers.
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- HanLP.
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