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中級レベル

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

約8時間で修了

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

英語

字幕:英語

学習内容

  • Check

    Build natural language processing systems using TensorFlow

  • Check

    Process text, including tokenization and representing sentences as vectors

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    Apply RNNs, GRUs, and LSTMs in TensorFlow

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    Train LSTMs on existing text to create original poetry and more

習得するスキル

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

次における1の1コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

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

約8時間で修了

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

英語

字幕:英語

シラバス - 本コースの学習内容

1
3時間で修了

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!

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13件のビデオ (合計30分), 1 reading, 3 quizzes
13件のビデオ
Using APIs2 分
Notebook for lesson 12 分
Text to sequence3 分
Looking more at the Tokenizer1 分
Padding2 分
Notebook for lesson 24 分
Sarcasm, really?2 分
Working with the Tokenizer1 分
Notebook for lesson 33 分
Week 1 Outro21
1件の学習用教材
News headlines dataset for sarcasm detection10 分
1の練習問題
Week 1 Quiz
2
3時間で修了

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.

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14件のビデオ (合計39分), 5 readings, 3 quizzes
14件のビデオ
Looking into the details4 分
How can we use vectors?2 分
More into the details2 分
Notebook for lesson 110 分
Remember the sarcasm dataset?1 分
Building a classifier for the sarcasm dataset1 分
Let’s talk about the loss function1 分
Pre-tokenized datasets43
Diving into the code (part 1)1 分
Diving into the code (part 2)2 分
Notebook for lesson 35 分
5件の学習用教材
IMDB reviews dataset10 分
Try it yourself10 分
TensoFlow datasets10 分
Subwords text encoder10 分
Week 2 Outro10 分
1の練習問題
Week 2 Quiz
3
3時間で修了

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!

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10件のビデオ (合計16分), 4 readings, 3 quizzes
10件のビデオ
LSTMs2 分
Implementing LSTMs in code1 分
Accuracy and loss1 分
A word from Laurence35
Looking into the code1 分
Using a convolutional network1 分
Going back to the IMDB dataset1 分
Tips from Laurence37
4件の学習用教材
Link to Andrew's sequence modeling course10 分
More info on LSTMs10 分
Exploring different sequence models10 分
Week 3 Outro10 分
1の練習問題
Week 3 Quiz
4
3時間で修了

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!

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14件のビデオ (合計27分), 3 readings, 3 quizzes
14件のビデオ
Training the data2 分
More on training the data1 分
Notebook for lesson 18 分
Finding what the next word should be2 分
Example1 分
Predicting a word1 分
Poetry!40
Looking into the code1 分
Laurence the poet!1 分
Your next task1 分
Outro, A conversation with Andrew Ng1 分
3件の学習用教材
link to Laurence's poetry10 分
Link to generating text using a character-based RNN10 分
Week 4 Outro10 分
1の練習問題
Week 4 Quiz
4.6
27件のレビューChevron Right

Natural Language Processing in TensorFlow からの人気レビュー

by 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.

by NCJul 15th 2019

This course will help you understand RNN even more. It is a must enrolled course after deeplearning course.

講師

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Laurence Moroney

AI Advocate
Google Brain

deeplearning.aiについて

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

TensorFlow in Practiceの専門講座について

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

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