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Промежуточный уровень

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

Прибл. 8 часа на выполнение

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

Английский

Субтитры: Английский

Чему вы научитесь

  • 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

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    Train LSTMs on existing text to create original poetry and more

Приобретаемые навыки

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

Курс 1 из 2 в программе

100% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Промежуточный уровень

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

Прибл. 8 часа на выполнение

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

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
3 ч. на завершение

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 видео ((всего 30 мин.)), 1 материал для самостоятельного изучения, 3 тестов
13 видео
Using APIs2мин
Notebook for lesson 12мин
Text to sequence3мин
Looking more at the Tokenizer1мин
Padding2мин
Notebook for lesson 24мин
Sarcasm, really?2мин
Working with the Tokenizer1мин
Notebook for lesson 33мин
Week 1 Outro21
1 материал для самостоятельного изучения
News headlines dataset for sarcasm detection10мин
1 практическое упражнение
Week 1 Quiz
Неделя
2
3 ч. на завершение

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 видео ((всего 39 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
14 видео
Looking into the details4мин
How can we use vectors?2мин
More into the details2мин
Notebook for lesson 110мин
Remember the sarcasm dataset?1мин
Building a classifier for the sarcasm dataset1мин
Let’s talk about the loss function1мин
Pre-tokenized datasets43
Diving into the code (part 1)1мин
Diving into the code (part 2)2мин
Notebook for lesson 35мин
5 материала для самостоятельного изучения
IMDB reviews dataset10мин
Try it yourself10мин
TensoFlow datasets10мин
Subwords text encoder10мин
Week 2 Outro10мин
1 практическое упражнение
Week 2 Quiz
Неделя
3
3 ч. на завершение

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 видео ((всего 16 мин.)), 4 материалов для самостоятельного изучения, 3 тестов
10 видео
LSTMs2мин
Implementing LSTMs in code1мин
Accuracy and loss1мин
A word from Laurence35
Looking into the code1мин
Using a convolutional network1мин
Going back to the IMDB dataset1мин
Tips from Laurence37
4 материала для самостоятельного изучения
Link to Andrew's sequence modeling course10мин
More info on LSTMs10мин
Exploring different sequence models10мин
Week 3 Outro10мин
1 практическое упражнение
Week 3 Quiz
Неделя
4
3 ч. на завершение

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 видео ((всего 27 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
14 видео
NLP W4 L1 ( part 3) - Training the data2мин
NLP W4 L1 ( part 3) - More on training the data1мин
SC L1 - Notebook for lesson 18мин
NLP W4 L2 (part 1) - Finding what the next word should be2мин
NLP W4 L2 (part 2) - Example1мин
NLP W4 L2 (part 3) - Predicting a word1мин
NLP W4 L3 (part 1) - Poetry!40
NLP W4 L3 ( part 2) Looking into the code1мин
NLP W4 L3 ( part 3) - Laurence the poet!1мин
NLP W4 L3 ( part 4) - Your next task1мин
Outro, A conversation with Andrew Ng1мин
3 материала для самостоятельного изучения
link to Laurence's poetry10мин
Link to generating text using a character-based RNN10мин
Week 4 Outro10мин
1 практическое упражнение
Week 4 Quiz
4.7
Рецензии: 19Chevron Right

Лучшие отзывы о курсе Natural Language Processing in TensorFlow

автор: 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.

автор: ASJun 29th 2019

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

Преподаватели

Avatar

Laurence Moroney

AI Advocate
Google Brain

О deeplearning.ai

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

О специализации ''TensorFlow in Practice'

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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