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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-for-natural-language-processing/
课程评论：没有评论
课程名称：深度学习在自然语言处理中的应用 课程概述：在本课程中，我们将深入探讨自然语言处理（NLP）领域。课程将展示深度学习如何通过词向量和嵌入、结构化深度学习、协同过滤、循环神经网络、序列到序列模型及变换网络等概念改变这一人工智能领域的面貌。我们主要关注语言符号的表示，尤其是单词或字符级别的表示，以及如何以语义合理的方式聚合它们，如句子或文档的表示。 课程开始时，我们将回顾传统的文本预处理流程及各种文本特征，包括基于词袋模型的二元特征和TF-IDF特征。随后，我们将深入探讨词向量和嵌入的概念，讨论著名的词嵌入技术，如word2vec、GloVe、Fasttext和ELMo。这将引导我们进入推荐系统的领域，以协同过滤和双塔模型为示例，展示嵌入在词表示之外的通用应用。 课程的第二部分将关注句子和序列表示。我们将处理自然语言处理的核心问题——语言建模，通过使用LSTM和GRU等循环模型达到统计和神经层面的理解。接下来，我们将讨论序列到序列模型，尤其是机器翻译这一旗舰NLP任务，同时也为讨论其他相似任务（如问答系统和聊天机器人）铺平道路。我们将介绍在循环序列到序列模型中注意力机制的核心思想，之后将其推广为一种通用深度学习概念。 这一推广引领我们进入了开创性变换网络的世界，它通过全注意力机制彻底改变了NLP领域。最后，我们将介绍NLP的“ImageNet时刻”，其中迁移学习与预训练的变换架构（如BERT、GPT系列、RoBERTa、ALBERT、XLTransformer和XLNet）相结合。
In this course, we will dive into the world of Natural Language Processing. We will demonstrate how Deep Learning has re-shaped this area of Artificial Intelligence using concepts like word vectors and embeddings, strucutured deep learning, collaborative filtering, recurrent neural networks, sequence-to-sequence models and transformer networks. In our journey, we will be mostly concerned with how to represent the language tokens, being at the word or character level, and and how to represent their aggregation, like sentences or documents, in a semantically sound way. We start the journey by going through the traditional pipeline of text pre-processing and the different text features like binary and TF-IDF features with the Bag-of-Words model. Then we will dive into the concepts of word vectors and embeddings as a general deep learning concept, with detailed discussion of famous word embedding techniques like word2vec, GloVe, Fasttext and ELMo. This will enable us to divert into recommender systems, using collaborative filtering and twin-tower model as an example of the generic usage of embeddings beyond word representations. In the second part of the course, we will be concerned with sentence and sequence representations. We will tackle the core NLP of Langauge Modeling, at statistical and neural levels, using recurrent models, like LSTM and GRU. In the following part, we tackle sequence-to-sequence models, with the flagship NLP task of Machine Translation, which paves the way to talk about many other tasks under the same design seq2seq pattern, like Question-Answering and Chatbots. We present the core idea idea of Attention mechanisms with recurrent seq2seq, before we generalize it as a generic deep learning concept. This generalization leads to the to the state-of-the art Transformer Network, which revolutionized the world of NLP, using full attention mechanisms. In the final part of the course, we present the ImageNet moment of NLP, where Transfer Learning comes into play together with pre-trained Transfomer architectures like BERT, GPT 1-2-3, RoBERTa, ALBERT, XLTransformer and XLNet.
