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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/nlp-sequence-models
课程评论：没有评论
课程名称：序列模型 课程概述：在深度学习专业化的第五门课程中，您将熟悉序列模型及其令人兴奋的应用，如语音识别、音乐合成、聊天机器人、机器翻译和自然语言处理（NLP）等。课程结束时，您将能够构建和训练递归神经网络（RNN）及其常用变体，如GRU和LSTM；应用RNN进行字符级语言建模；积累自然语言处理和词嵌入的经验；使用HuggingFace分词器和变换器模型解决诸如命名实体识别（NER）和问答等不同的NLP任务。 深度学习专业化是一个基础性程序，旨在帮助您理解深度学习的能力、挑战和后果，并为您参与尖端AI技术的开发做好准备。该课程为您在AI领域迈出决定性的一步提供了途径，帮助您获得提升职业发展的知识和技能。 课程大纲： 1. 递归神经网络：了解递归神经网络，这种模型在时间序列数据上表现优异，以及它的几种变体，包括LSTM、GRU和双向RNN。 2. 自然语言处理与词嵌入：深度学习与自然语言处理的结合极为强大。使用词向量表示和嵌入层，训练递归神经网络在情感分析、命名实体识别和神经机器翻译等多种应用中的出色表现。 3. 序列模型与注意力机制：通过引入注意力机制增强您的序列模型，这是一种算法，可帮助您的模型在给定输入序列的情况下决定关注的重点。然后，探索语音识别及如何处理音频数据。 4. 变换器网络：详细了解变换器网络的设计和应用。 这个课程将为您提供实现先进深度学习技术所需的技能。
Name:Recurrent Neural Networks
Description:Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
Name:Natural Language Processing & Word Embeddings
Description:Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
Name:Sequence Models & Attention Mechanism
Description:Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
Name:Transformer Network
Description:
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
