Therefore, they extract relevant information from words and sentences. In natural language processing computers try to analyze and understand human language for the purpose of performing useful tasks. 14.2 Improvements of the Self-Attention mechanismĢ.1 Word Embeddings and Neural Network Language Models.9.3.3 The problem of Standard Parameterization.9.3.2 Permutation Language Modeling(PLM).9.2.1 Auto-regressive Language Model(AR).9.1 Bidirectional Encoder Representations from Transformers (BERT).8.1.3 Computational Difference between Luong- and Bahdanau-Attention.7.3.3 GPT - First step towards transformers.7.3.2 ULMFiT - cutting-edge model using LSTMs.7.3.1 ELMo - The “new age” of embeddings.7.2.2 Feature Extraction vs. Fine-tuning.7.2 Sequential inductive transfer learning.6 Introduction: Transfer Learning for NLP.5.3 Datasets and Experimental Evaluation.5.2.1 CNN-rand/CNN-static/CNN-non-static/CNN-multichannel.5.1 Introduction to Basic Architecture of CNN.5 Convolutional neural networks and their applications in NLP.4.1.3 Vanishing and Exploding Gradients.4.1.1 Network Structure and Forwardpropagation.4.1 Structure and Training of Simple RNNs.4 Recurrent neural networks and their applications in NLP.
3.3 Hyperparameter Tuning and System Design Choices.3.2.1 Feedforward Neural Network Language Model (NNLM).3 Foundations/Applications of Modern NLP.2.1 Word Embeddings and Neural Network Language Models.Modern Approaches in Natural Language Processing.
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