Deep Learning for Natural Language Processing, 2nd Edition

سرفصل های دوره

Introduction

001. Deep Learning for Natural Language Processing Introduction

Lesson 1 The Power and Elegance of Deep Learning for NLP

001. Topics

002. 1.1 Introduction to Deep Learning for Natural Language Processing

003. 1.2 Running the Hands-On Code Examples in Jupyter Notebooks

004. 1.3 Review of Prerequisite Deep Learning Theory

005. 1.4 A Sneak Peak

Lesson 2 Word Vectors

001. Topics

002. 2.1 Computational Representations of Natural Language Elements

003. 2.2 Visualizing Word Vectors with word2viz

004. 2.3 Localist versus Distributed Representations

005. 2.4 Elements of Natural Human Language

006. 2.5 The word2vec Algorithm

007. 2.6 Creating Word Vectors with word2vec

008. 2.7 Pre-Trained Word Vectors and doc2vec

Lesson 3 Modeling Natural Language Data

001. Topics

002. 3.1 Best Practices for Preprocessing Natural Language Data

003. 3.2 The Area Under the ROC Curve

004. 3.3 Document Classification with a Dense Neural Net

005. 3.4 Classification with a Convolutional Neural Net

Lesson 4 Recurrent Neural Networks

001. Topics

002. 4.1 Essential Theory of RNNs

003. 4.2 RNNs in Practice

004. 4.3 Essential Theory of LSTMs and GRUs

005. 4.4 LSTMs and GRUs in Practice

Lesson 5 Advanced Models

001. Topics

002. 5.1 Bidirectional LSTMs

003. 5.2 Stacked LSTMs

004. 5.3 Datasets for NLP

005. 5.4 Sequence Generation

006. 5.5 seq2seq and Attention

007. 5.6 Transfer Learning in NLP BERT, ELMo, GPT-2 and Other Characters

008. 5.7 Non-Sequential Architectures The Keras Functional API

009. 5.8 (Financial) Time Series Applications

Summary

001. Deep Learning for Natural Language Processing Summary

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