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.
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课程信息
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您将学到的内容有
Build natural language processing systems using TensorFlow
Process text, including tokenization and representing sentences as vectors
Apply RNNs, GRUs, and LSTMs in TensorFlow
Train LSTMs on existing text to create original poetry and more
您将获得的技能
Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs
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第 3 门课程（共 4 门）
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中级
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
完成时间大约为9 小时
建议：4 weeks of study, 4-5 hours/week
英语（English）
字幕：英语（English）, 韩语
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
Sentiment in text
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!
完成时间为 3 小时
13 个视频 （总计 30 分钟）, 1 个阅读材料, 3 个测验
13 个视频
Introduction1分钟
Word based encodings2分钟
Using APIs2分钟
Notebook for lesson 12分钟
Text to sequence3分钟
Looking more at the Tokenizer1分钟
Padding2分钟
Notebook for lesson 24分钟
Sarcasm, really?2分钟
Working with the Tokenizer1分钟
Notebook for lesson 33分钟
Week 1 Wrap up21
1 个阅读材料
News headlines dataset for sarcasm detection10分钟
1 个练习
Week 1 Quiz
完成时间为 3 小时
Word Embeddings
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 udnerstand 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.
完成时间为 3 小时
14 个视频 （总计 39 分钟）, 5 个阅读材料, 3 个测验
14 个视频
Introduction2分钟
The IMBD dataset1分钟
Looking into the details4分钟
How can we use vectors?2分钟
More into the details2分钟
Notebook for lesson 110分钟
Remember the sarcasm dataset?1分钟
Building a classifier for the sarcasm dataset1分钟
Let’s talk about the loss function1分钟
Pre-tokenized datasets43
Diving into the code (part 1)1分钟
Diving into the code (part 2)2分钟
Notebook for lesson 35分钟
5 个阅读材料
IMDB reviews dataset10分钟
Try it yourself10分钟
TensoFlow datasets10分钟
Subwords text encoder10分钟
Week 2 Wrap up10分钟
1 个练习
Week 2 Quiz
完成时间为 3 小时
Sequence models
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!
完成时间为 3 小时
10 个视频 （总计 16 分钟）, 4 个阅读材料, 3 个测验
10 个视频
Introduction2分钟
LSTMs2分钟
Implementing LSTMs in code1分钟
Accuracy and loss1分钟
A word from Laurence35
Looking into the code1分钟
Using a convolutional network1分钟
Going back to the IMDB dataset1分钟
Tips from Laurence37
4 个阅读材料
Link to Andrew's sequence modeling course10分钟
More info on LSTMs10分钟
Exploring different sequence models10分钟
Week 3 Wrap up10分钟
1 个练习
Week 3 Quiz
完成时间为 3 小时
Sequence models and literature
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!
完成时间为 3 小时
14 个视频 （总计 27 分钟）, 3 个阅读材料, 3 个测验
14 个视频
Introduction1分钟
Looking into the code57
Training the data2分钟
More on training the data1分钟
Notebook for lesson 18分钟
Finding what the next word should be2分钟
Example1分钟
Predicting a word1分钟
Poetry!40
Looking into the code1分钟
Laurence the poet!1分钟
Your next task1分钟
A conversation with Andrew Ng1分钟
3 个阅读材料
link to Laurence's poetry10分钟
Link to generating text using a character-based RNN10分钟
Wrap up10分钟
1 个练习
Week 4 Quiz
审阅
4.6
来自NATURAL LANGUAGE PROCESSING IN TENSORFLOW的热门评论
由 GS 提供Aug 26, 2019
Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
由 DW 提供Nov 19, 2019
These classes are excelling practical examples of how to use tensorflow for various problem types. My only objection is they are slightly light on the actual, behind the scenes, math and intuition.
由 MU 提供Feb 28, 2020
Excellent course\n\nGave me a brief idea with practical experience about how to process strings for machine learning.\n\nI would like to thank Laurence Sir\n\nand a Special thanks to Andrew Sir
由 SB 提供Aug 18, 2019
Good course that gives you basic understanding of word embeddings, sequence analysis, and many other things. Thank you for Mr. Moroney and the entire Coursera team for making it available.
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关于 TensorFlow in Practice 专项课程
Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry!
AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
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