One-hot encoded book titles

PyBooks wants to catalog and analyze the book genres in its library. Apply one-hot encoding to a list of book genres to make them machine-readable.

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Deep Learning for Text with PyTorch

Anleitung zur Übung

Define the size of the vocabulary and save to vocab_size.
Create one-hot vectors using the appropriate torch technique and vocab_size.
Create a dictionary mapping genres to their corresponding one-hot vectors using dictionary comprehension; the dictionary keys should be the genre.

Interaktive Übung

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genres = ['Fiction','Non-fiction','Biography', 'Children','Mystery']

# Define the size of the vocabulary
vocab_size = ____(____)

# Create one-hot vectors
one_hot_vectors = torch.____(____)

# Create a dictionary mapping genres to their one-hot vectors
one_hot_dict = {____: ____[i] for i, genre in enumerate(genres)}

for genre, vector in one_hot_dict.items():
    print(f'{genre}: {vector.numpy()}')

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Diese Übung ist Teil des Kurses

Deep Learning for Text with PyTorch

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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: Introduction to preprocessing for text Exercise 2: Word frequency analysis Exercise 3: Preprocessing text Exercise 4: Encoding text data Exercise 5: One-hot encoded book titles

Aktuelle Übung

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Exercise 1: Overview of Text Classification Exercise 2: Embedding in PyTorch Exercise 3: Categorizing text classification tasks Exercise 4: Convolutional neural networks for text classification Exercise 5: Build a CNN model for text Exercise 6: Train a CNN model for text Exercise 7: Testing the Sentiment Analysis CNN Model Exercise 8: Recurrent neural networks for text classification Exercise 9: Building an RNN model for text Exercise 10: Building an LSTM model for text Exercise 11: Building a GRU model for text Exercise 12: Evaluation metrics for text classification Exercise 13: Evaluating RNN classification models Exercise 14: Evaluating the model's performance Exercise 15: Comparing models

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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

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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