|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/natural-language-processing-tensorflow
课程评论：没有评论
课程名称：使用TensorFlow进行自然语言处理 课程概述：如果您是一名希望构建可扩展的AI算法的软件开发人员，那么您需要了解如何使用工具来实现这些目标。本课程将教授您使用TensorFlow这一流行的开源机器学习框架的最佳实践。在deeplearning.ai TensorFlow专业课程的第三部分中，您将学习如何使用TensorFlow构建自然语言处理系统。课程内容包括文本处理、分词、以及将句子表示为向量，使其可以作为神经网络的输入。同时，您还将学习在TensorFlow中应用RNN、GRU和LSTM。最后，您将能够在现有文本上训练LSTM，以创作原创诗歌。 本课程是Andrew Ng的机器学习课程和深度学习专业课程的延续，这些课程教授机器学习和深度学习的最重要和基础原则。新的deeplearning.ai TensorFlow专业课程将教您如何使用TensorFlow实现这些原则，以便您能够开始构建和应用可扩展模型来解决现实世界中的问题。为了加深对神经网络工作原理的理解，我们建议您参加深度学习专业课程。 课程大纲： 第一部分：文本情感分析 描述：理解文本情感的第一步是对文本进行分词。在本周，您将学习如何使用TensorFlow中的Tokenizer和pad_sequences API准备和编码文本，为训练神经网络做好准备。 第二部分：词嵌入 描述：本周您将了解词嵌入技术，将数字标记映射到高维空间中的向量。通过这些嵌入和标记示例，您将开始训练神经网络来理解文本中的情感，特别是分析电影评论中推动积极或消极含义的单词。 第三部分：序列模型 描述：本周您将深入多种模型格式，学习如何训练模型理解单词在序列中的上下文，了解情感表达不仅受单词影响，还受到单词顺序的影响。 第四部分：序列模型与文学 描述：本周您将利用所学知识进行预测，通过训练生成诗歌，基于传统爱尔兰歌曲的歌词，生成美妙的诗句。 通过本课程，您将掌握使用TensorFlow进行自然语言处理的核心技能，并能够在实际应用中创造出富有成效的AI解决方案。
Part: 1
Title:Sentiment in text
Description:The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!
Part: 2
Title: Word Embeddings
Description:Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens, and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.
Part: 3
Title:Sequence models
Description:In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!
Part: 4
Title:Sequence models and literature
Description:Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
