À propos de ce cours
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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

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

Approx. 8 heures pour terminer

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

Anglais

Sous-titres : Anglais

Ce que vous allez apprendre

  • 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

Compétences que vous acquerrez

Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

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

Approx. 8 heures pour terminer

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

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
3 heures pour terminer

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 vidéos (Total 30 min), 1 lecture, 3 quiz
13 vidéos
Using APIs2 min
Notebook for lesson 12 min
Text to sequence3 min
Looking more at the Tokenizer1 min
Padding2 min
Notebook for lesson 24 min
Sarcasm, really?2 min
Working with the Tokenizer1 min
Notebook for lesson 33 min
Week 1 Outro21s
1 lecture
News headlines dataset for sarcasm detection10 min
1 exercice pour s'entraîner
Week 1 Quiz
Semaine
2
3 heures pour terminer

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 vidéos (Total 39 min), 5 lectures, 3 quiz
14 vidéos
Looking into the details4 min
How can we use vectors?2 min
More into the details2 min
Notebook for lesson 110 min
Remember the sarcasm dataset?1 min
Building a classifier for the sarcasm dataset1 min
Let’s talk about the loss function1 min
Pre-tokenized datasets43s
Diving into the code (part 1)1 min
Diving into the code (part 2)2 min
Notebook for lesson 35 min
5 lectures
IMDB reviews dataset10 min
Try it yourself10 min
TensoFlow datasets10 min
Subwords text encoder10 min
Week 2 Outro10 min
1 exercice pour s'entraîner
Week 2 Quiz
Semaine
3
3 heures pour terminer

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 vidéos (Total 16 min), 4 lectures, 3 quiz
10 vidéos
LSTMs2 min
Implementing LSTMs in code1 min
Accuracy and loss1 min
A word from Laurence35s
Looking into the code1 min
Using a convolutional network1 min
Going back to the IMDB dataset1 min
Tips from Laurence37s
4 lectures
Link to Andrew's sequence modeling course10 min
More info on LSTMs10 min
Exploring different sequence models10 min
Week 3 Outro10 min
1 exercice pour s'entraîner
Week 3 Quiz
Semaine
4
3 heures pour terminer

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 vidéos (Total 27 min), 3 lectures, 3 quiz
14 vidéos
NLP W4 L1 ( part 3) - Training the data2 min
NLP W4 L1 ( part 3) - More on training the data1 min
SC L1 - Notebook for lesson 18 min
NLP W4 L2 (part 1) - Finding what the next word should be2 min
NLP W4 L2 (part 2) - Example1 min
NLP W4 L2 (part 3) - Predicting a word1 min
NLP W4 L3 (part 1) - Poetry!40s
NLP W4 L3 ( part 2) Looking into the code1 min
NLP W4 L3 ( part 3) - Laurence the poet!1 min
NLP W4 L3 ( part 4) - Your next task1 min
Outro, A conversation with Andrew Ng1 min
3 lectures
link to Laurence's poetry10 min
Link to generating text using a character-based RNN10 min
Week 4 Outro10 min
1 exercice pour s'entraîner
Week 4 Quiz
4.7
19 avisChevron Right

Principaux examens pour Natural Language Processing in TensorFlow

par 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.

par ASJun 29th 2019

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

Enseignant

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Laurence Moroney

AI Advocate
Google Brain

À propos de deeplearning.ai

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

À propos de la Spécialisation 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

Foire Aux Questions

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  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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