Acerca de este Curso
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Fechas límite flexibles

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Nivel intermedio

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...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • 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 obtendrás

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

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...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
3 horas para completar

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 videos (Total 30 minutos), 1 reading, 3 quizzes
13 videos
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 Outro21s
1 lectura
News headlines dataset for sarcasm detection10m
1 ejercicio de práctica
Week 1 Quiz
Semana
2
3 horas para completar

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 minutos), 5 readings, 3 quizzes
14 videos
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 datasets43s
Diving into the code (part 1)1m
Diving into the code (part 2)2m
Notebook for lesson 35m
5 lecturas
IMDB reviews dataset10m
Try it yourself10m
TensoFlow datasets10m
Subwords text encoder10m
Week 2 Outro10m
1 ejercicio de práctica
Week 2 Quiz
Semana
3
3 horas para completar

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 videos (Total 16 minutos), 4 readings, 3 quizzes
10 videos
Implementing LSTMs in code1m
Accuracy and loss1m
A word from Laurence35s
Looking into the code1m
Using a convolutional network1m
Going back to the IMDB dataset1m
Tips from Laurence37s
4 lecturas
Link to Andrew's sequence modeling course10m
More info on LSTMs10m
Exploring different sequence models10m
Week 3 Outro10m
1 ejercicio de práctica
Week 3 Quiz
Semana
4
3 horas para completar

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 minutos), 3 readings, 3 quizzes
14 videos
Training the data2m
More on training the data1m
Notebook for lesson 18m
Finding what the next word should be2m
Example1m
Predicting a word1m
Poetry!40s
Looking into the code1m
Laurence the poet!1m
Your next task1m
Outro, A conversation with Andrew Ng1m
3 lecturas
link to Laurence's poetry10m
Link to generating text using a character-based RNN10m
Week 4 Outro10m
1 ejercicio de práctica
Week 4 Quiz
4.6
27 revisionesChevron Right

Principales revisiones sobre 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 NCJul 15th 2019

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

Instructor

Avatar

Laurence Moroney

AI Advocate
Google Brain

Acerca de deeplearning.ai

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

Acerca del programa especializado 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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  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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