Kitap açıklamalarına TF-IDF uygulama

PyBooks, birkaç kitap açıklaması topladı ve bunlardaki önemli kelimeleri TF-IDF kodlama tekniğiyle belirlemek istiyor. Bunu yaparak, her bir kitabın kendine özgü özellikleri hakkında daha fazla içgörü edinmeyi ve kitap öneri sistemlerine yardımcı olmayı umuyorlar.

Aşağıdaki paketler senin için içe aktarıldı: torch, torchtext.

Bu egzersiz

PyTorch ile Metin için Deep Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Ham doküman koleksiyonunu TF-IDF özelliklerinden oluşan bir matrise dönüştüren sklearn.feature_extraction.text içindeki TfidfVectorizer sınıfını içe aktar.
Bu sınıftan bir nesne oluştur ve bu nesneyi kullanarak descriptions değişkenini TF-IDF vektör matrisine kodla.
vectorizer üzerinden ilk beş özellik adını ve tfidf_encoded_descriptions içindeki kodlanmış vektörleri alıp görüntüle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Importing TF-IDF from sklearn
from sklearn.feature_extraction.text import ____

# Initialize TF-IDF encoding vectorizer
vectorizer = ____()
tfidf_encoded_descriptions = vectorizer.____(descriptions)

# Extract and print the first five features
print(____.get_feature_names_out()[:5])
print(____.toarray()[0, :5])

Kodu Düzenle ve Çalıştır

Bu egzersiz

PyTorch ile Metin için Deep Learning

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

This chapter introduces you to deep learning for text and its applications. Learn how to use PyTorch for text processing and get hands-on experience with techniques such as tokenization, stemming, stopword removal, and more. Understand the importance of encoding text data and implement encoding techniques using PyTorch. Finally, consolidate your knowledge by building a text processing pipeline combining these techniques.

Exercise 1: Metin için ön işleme giriş Exercise 2: Kelime sıklığı analizi Exercise 3: Metni ön işleme Exercise 4: Metin verisini kodlama Exercise 5: One-hot kodlanmış kitap başlıkları Exercise 6: Kitap başlıkları için Bag-of-words Exercise 7: Kitap açıklamalarına TF-IDF uygulama

Geçerli Egzersiz

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Exercise 1: Introduction to text generation Exercise 2: Creating a RNN model for text generation Exercise 3: Text generation using RNN - Training and Generation Exercise 4: Generative adversarial networks for text generation Exercise 5: Building a generator and discriminator Exercise 6: Training a GAN model Exercise 7: Pre-trained models for text generation Exercise 8: Text completion with pre-trained GPT-2 models Exercise 9: Language translation with pretrained PyTorch model Exercise 10: Evaluation metrics for text generation Exercise 11: Evaluating pretrained text generation model Exercise 12: Understanding text generation metrics

Understand the concept of transfer learning and its application in text classification. Explore Transformers, their architecture, and how to use them for text classification and generation tasks. You will also delve into attention mechanisms and their role in text processing. Finally, understand the potential impacts of adversarial attacks on text classification models and learn how to protect your models.

Exercise 1: Transfer learning for text classification Exercise 2: Transfer learning using BERT Exercise 3: Evaluating the BERT model Exercise 4: Transformers for text processing Exercise 5: Creating a transformer model Exercise 6: Training and testing the Transformer model Exercise 7: Attention mechanisms for text processing Exercise 8: Creating a RNN model with attention Exercise 9: Training and testing the RNN model with attention Exercise 10: Adversarial attacks on text classification models Exercise 11: Adversarial attack classification Exercise 12: Safeguarding AI at PyBooks Exercise 13: Wrap-up