20 News Groups veri kümesini keşfetme

Bu egzersizde, sklearn.datasets içindeki fetch_20newsgroups() fonksiyonu kullanılarak elde edilen 20 News Groups veri kümesinden, yalnızca üç sınıf filtrelenmiş bir örnek verilecektir: sci.space, alt.atheism ve soc.religion.christian.

Veri kümesi news_dataset değişkenine yüklenmiştir. Öznitelikleri, konsolda keşfedebilmen için yazdırılmıştır.

Bu fonksiyonun nasıl kullanılacağı hakkında daha fazla bilgi için Sklearn dokümantasyonuna bakabilirsin.

Dönüşümlerin nasıl gerçekleştiğini anlamak için metinleri adım adım tokenize edecek ve etiketleri one-hot olarak kodlayacaksın.

Bu egzersiz

Keras ile Dil Modellemesi için Yinelenen Sinir Ağları (RNN)

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# See example article
print(news_dataset.____[5])

Kodu Düzenle ve Çalıştır

Bu egzersiz

Keras ile Dil Modellemesi için Yinelenen Sinir Ağları (RNN)

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

Exercise 1: Introduction to the course Exercise 2: Comparing the number of parameter of RNN and ANN Exercise 3: Sentiment analysis Exercise 4: Sequence to sequence models Exercise 5: Introduction to language models Exercise 6: Getting used to text data Exercise 7: Preparing text data for model input Exercise 8: Transforming new text Exercise 9: Introduction to RNN inside Keras Exercise 10: Keras models Exercise 11: Keras preprocessing Exercise 12: Your first RNN model

You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

Exercise 1: Vanishing and exploding gradients Exercise 2: Exploding gradient problem Exercise 3: Vanishing gradient problem Exercise 4: GRU and LSTM cells Exercise 5: GRU cells are better than simpleRNN Exercise 6: Stacking RNN layers Exercise 7: The Embedding layer Exercise 8: Number of parameters comparison Exercise 9: Transfer learning Exercise 10: Embeddings improves performance Exercise 11: Sentiment classification revisited Exercise 12: Better sentiment classification Exercise 13: Using the CNN layer

Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

Exercise 1: Veri ön işleme Exercise 2: Etiket vektörlerini hazırla Exercise 3: Veriyi ön işle Exercise 4: Dil modelleri için transfer öğrenmesi Exercise 5: Transfer learning başlangıç noktası Exercise 6: Word2Vec Exercise 7: Çok sınıflı sınıflandırma modelleri Exercise 8: 20 News Groups veri kümesini keşfetme

Geçerli Egzersiz

Exercise 9: Haber makalelerini sınıflandırma Exercise 10: Modelin performansını değerlendirme Exercise 11: Kesinlik-Duyarlılık dengesi Exercise 12: Precision mı Recall mı, işte bütün mesele Exercise 13: Çok sınıflı sınıflandırmada performans

This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

Exercise 1: Sequence to Sequence Models Exercise 2: Text generation examples Exercise 3: NMT example Exercise 4: The Text Generating Function Exercise 5: Predict next character Exercise 6: Generate sentence with context Exercise 7: Change the probability scale Exercise 8: Text Generation Models Exercise 9: Create vectors of sentences and next characters Exercise 10: Preparing the data for training Exercise 11: Creating the text generation model Exercise 12: Neural Machine Translation Exercise 13: Preparing the input text Exercise 14: Preparing the output text Exercise 15: Translate Portuguese to English Exercise 16: Congratulations!