Informações sobre o curso
249,630 visualizações recentes

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

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

Aprox. 8 horas para completar

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

Inglês

Legendas: Inglês

O que você vai aprender

  • 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

Habilidades que você terá

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

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

Aprox. 8 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
3 horas para concluir

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 vídeos ((Total 30 mín.)), 1 leitura, 3 testes
13 videos
Using APIs2min
Notebook for lesson 12min
Text to sequence3min
Looking more at the Tokenizer1min
Padding2min
Notebook for lesson 24min
Sarcasm, really?2min
Working with the Tokenizer1min
Notebook for lesson 33min
Week 1 Outro21s
1 leituras
News headlines dataset for sarcasm detection10min
1 exercício prático
Week 1 Quiz
Semana
2
3 horas para concluir

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 vídeos ((Total 39 mín.)), 5 leituras, 3 testes
14 videos
Looking into the details4min
How can we use vectors?2min
More into the details2min
Notebook for lesson 110min
Remember the sarcasm dataset?1min
Building a classifier for the sarcasm dataset1min
Let’s talk about the loss function1min
Pre-tokenized datasets43s
Diving into the code (part 1)1min
Diving into the code (part 2)2min
Notebook for lesson 35min
5 leituras
IMDB reviews dataset10min
Try it yourself10min
TensoFlow datasets10min
Subwords text encoder10min
Week 2 Outro10min
1 exercício prático
Week 2 Quiz
Semana
3
3 horas para concluir

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 vídeos ((Total 16 mín.)), 4 leituras, 3 testes
10 videos
LSTMs2min
Implementing LSTMs in code1min
Accuracy and loss1min
A word from Laurence35s
Looking into the code1min
Using a convolutional network1min
Going back to the IMDB dataset1min
Tips from Laurence37s
4 leituras
Link to Andrew's sequence modeling course10min
More info on LSTMs10min
Exploring different sequence models10min
Week 3 Outro10min
1 exercício prático
Week 3 Quiz
Semana
4
3 horas para concluir

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 vídeos ((Total 27 mín.)), 3 leituras, 3 testes
14 videos
NLP W4 L1 ( part 3) - Training the data2min
NLP W4 L1 ( part 3) - More on training the data1min
SC L1 - Notebook for lesson 18min
NLP W4 L2 (part 1) - Finding what the next word should be2min
NLP W4 L2 (part 2) - Example1min
NLP W4 L2 (part 3) - Predicting a word1min
NLP W4 L3 (part 1) - Poetry!40s
NLP W4 L3 ( part 2) Looking into the code1min
NLP W4 L3 ( part 3) - Laurence the poet!1min
NLP W4 L3 ( part 4) - Your next task1min
Outro, A conversation with Andrew Ng1min
3 leituras
link to Laurence's poetry10min
Link to generating text using a character-based RNN10min
Week 4 Outro10min
1 exercício prático
Week 4 Quiz
4.7
19 avaliaçõesChevron Right

Principais avaliações do Natural Language Processing in TensorFlow

por 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.

por ASJun 29th 2019

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

Instrutores

Avatar

Laurence Moroney

AI Advocate
Google Brain

Sobre deeplearning.ai

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

Sobre o Programa de cursos integrados 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.