About this Course
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다음 전문 분야의 2개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

완료하는 데 약 8시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

배울 내용

  • Check

    Build natural language processing systems using TensorFlow

  • Check

    Process text, including tokenization and representing sentences as vectors

  • Check

    Apply RNNs, GRUs, and LSTMs in TensorFlow

  • Check

    Train LSTMs on existing text to create original poetry and more

귀하가 습득할 기술

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

다음 전문 분야의 2개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

완료하는 데 약 8시간 필요

권장: 4 weeks of study, 4-5 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

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!

...
13 videos (Total 30 min), 1 reading, 3 quizzes
13개의 동영상
Using APIs2m
Notebook for lesson 12m
Text to sequence3m
Looking more at the Tokenizer1m
Padding2m
Notebook for lesson 24m
Sarcasm, really?2m
Working with the Tokenizer1m
Notebook for lesson 33m
Week 1 Outro21
1개의 읽기 자료
News headlines dataset for sarcasm detection10m
1개 연습문제
Week 1 Quiz
2
완료하는 데 3시간 필요

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.

...
14 videos (Total 39 min), 5 readings, 3 quizzes
14개의 동영상
Looking into the details4m
How can we use vectors?2m
More into the details2m
Notebook for lesson 110m
Remember the sarcasm dataset?1m
Building a classifier for the sarcasm dataset1m
Let’s talk about the loss function1m
Pre-tokenized datasets43
Diving into the code (part 1)1m
Diving into the code (part 2)2m
Notebook for lesson 35m
5개의 읽기 자료
IMDB reviews dataset10m
Try it yourself10m
TensoFlow datasets10m
Subwords text encoder10m
Week 2 Outro10m
1개 연습문제
Week 2 Quiz
3
완료하는 데 3시간 필요

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!

...
10 videos (Total 16 min), 4 readings, 3 quizzes
10개의 동영상
Implementing LSTMs in code1m
Accuracy and loss1m
A word from Laurence35
Looking into the code1m
Using a convolutional network1m
Going back to the IMDB dataset1m
Tips from Laurence37
4개의 읽기 자료
Link to Andrew's sequence modeling course10m
More info on LSTMs10m
Exploring different sequence models10m
Week 3 Outro10m
1개 연습문제
Week 3 Quiz
4
완료하는 데 3시간 필요

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!

...
14 videos (Total 27 min), 3 readings, 3 quizzes
14개의 동영상
NLP W4 L1 ( part 3) - Training the data2m
NLP W4 L1 ( part 3) - More on training the data1m
SC L1 - Notebook for lesson 18m
NLP W4 L2 (part 1) - Finding what the next word should be2m
NLP W4 L2 (part 2) - Example1m
NLP W4 L2 (part 3) - Predicting a word1m
NLP W4 L3 (part 1) - Poetry!40
NLP W4 L3 ( part 2) Looking into the code1m
NLP W4 L3 ( part 3) - Laurence the poet!1m
NLP W4 L3 ( part 4) - Your next task1m
Outro, A conversation with Andrew Ng1m
3개의 읽기 자료
link to Laurence's poetry10m
Link to generating text using a character-based RNN10m
Week 4 Outro10m
1개 연습문제
Week 4 Quiz
4.7
19개의 리뷰Chevron Right

Natural Language Processing in TensorFlow의 최상위 리뷰

대학: GIJun 22nd 2019

Amazing course by Laurence Moroney. But only after finishing Sequence Models by Andrew NG, I was able to understand the concepts taught here.

대학: ASJun 29th 2019

Helped me in understanding how to use Tensorflow for NLP with Keras API

강사

Avatar

Laurence Moroney

AI Advocate
Google Brain

deeplearning.ai 정보

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

TensorFlow in Practice 전문 분야 정보

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. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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