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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課程信息
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您將學到的內容有
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
您將獲得的技能
Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 4 門）
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中級
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
完成時間大約為9 小時
建議：4 weeks of study, 4-5 hours/week
英語（English）
字幕：英語（English）, 韓語
提供方
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 3 小時
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!
完成時間為 3 小時
13 個視頻 （總計 30 分鐘）, 1 個閱讀材料, 3 個測驗
13 個視頻
Introduction1分鐘
Word based encodings2分鐘
Using APIs2分鐘
Notebook for lesson 12分鐘
Text to sequence3分鐘
Looking more at the Tokenizer1分鐘
Padding2分鐘
Notebook for lesson 24分鐘
Sarcasm, really?2分鐘
Working with the Tokenizer1分鐘
Notebook for lesson 33分鐘
Week 1 Wrap up21
1 個閱讀材料
News headlines dataset for sarcasm detection10分鐘
1 個練習
Week 1 Quiz
完成時間為 3 小時
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.
完成時間為 3 小時
14 個視頻 （總計 39 分鐘）, 5 個閱讀材料, 3 個測驗
14 個視頻
Introduction2分鐘
The IMBD dataset1分鐘
Looking into the details4分鐘
How can we use vectors?2分鐘
More into the details2分鐘
Notebook for lesson 110分鐘
Remember the sarcasm dataset?1分鐘
Building a classifier for the sarcasm dataset1分鐘
Let’s talk about the loss function1分鐘
Pre-tokenized datasets43
Diving into the code (part 1)1分鐘
Diving into the code (part 2)2分鐘
Notebook for lesson 35分鐘
5 個閱讀材料
IMDB reviews dataset10分鐘
Try it yourself10分鐘
TensoFlow datasets10分鐘
Subwords text encoder10分鐘
Week 2 Wrap up10分鐘
1 個練習
Week 2 Quiz
完成時間為 3 小時
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!
完成時間為 3 小時
10 個視頻 （總計 16 分鐘）, 4 個閱讀材料, 3 個測驗
10 個視頻
Introduction2分鐘
LSTMs2分鐘
Implementing LSTMs in code1分鐘
Accuracy and loss1分鐘
A word from Laurence35
Looking into the code1分鐘
Using a convolutional network1分鐘
Going back to the IMDB dataset1分鐘
Tips from Laurence37
4 個閱讀材料
Link to Andrew's sequence modeling course10分鐘
More info on LSTMs10分鐘
Exploring different sequence models10分鐘
Week 3 Wrap up10分鐘
1 個練習
Week 3 Quiz
完成時間為 3 小時
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!
完成時間為 3 小時
14 個視頻 （總計 27 分鐘）, 3 個閱讀材料, 3 個測驗
14 個視頻
Introduction1分鐘
Looking into the code57
Training the data2分鐘
More on training the data1分鐘
Notebook for lesson 18分鐘
Finding what the next word should be2分鐘
Example1分鐘
Predicting a word1分鐘
Poetry!40
Looking into the code1分鐘
Laurence the poet!1分鐘
Your next task1分鐘
A conversation with Andrew Ng1分鐘
3 個閱讀材料
link to Laurence's poetry10分鐘
Link to generating text using a character-based RNN10分鐘
Wrap up10分鐘
1 個練習
Week 4 Quiz
審閱
4.6
來自NATURAL LANGUAGE PROCESSING IN TENSORFLOW的熱門評論
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.
由 AM 提供Sep 22, 2019
Excellent course. Teaches NLP thoroughly, going from the basics such as tokenization and padding to complex topics such as word embeddings and sequence models (like RNNs, LSTMs and GRUs).
由 CL 提供Aug 4, 2019
Great course with fun examples! Probably more valuable after completing Deep Learning Specialization/Sequence Models by Andrew Ng (https://www.coursera.org/learn/nlp-sequence-models)
由 A 提供Dec 29, 2019
This is good course for those who are want to practice in natural language processing in Tensor Flow and also learned sentiment analysis it is having wonderful stuff for beginners
由 A 提供Dec 18, 2019
Excellent course. Teaches NLP thoroughly, going from the basics such as tokenization and padding to complex topics such as word embeddings and sequence models (like RNNs, LSTMs)
由 SS 提供Aug 5, 2019
This course covers the overview of NLP without going into much mathematical detail. In short time span, many things can be learnt from this course, helpful for the beginners.
由 RG 提供Jan 23, 2020
Very nice course. However there were issues with the notebooks from week 3. Some of the code didn't work, and also some of the notebooks titles seem to have been swapped up.
由 DS 提供Mar 16, 2020
This course has helped me understand, how the basics of NLP work. I have gained insight on how to start implementing an NLP model, based on what is to be predicted
由 PR 提供Oct 18, 2019
The first time ever that I found a NLP course interesting, and short and easy to understand. Most courses use lot of jargon and make it way too hard to understand.
關於 TensorFlow in Practice 專項課程
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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