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PyTorch로 배우는 텍스트 딥러닝

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업데이트됨 2026. 1.

PyTorch로 텍스트용 Deep Learning을 배우고, 자연어 처리와 텍스트 생성의 새로운 가능성을 열어 보세요.

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PyTorchArtificial Intelligence4시간16 동영상50 연습 문제4,050 XP9,592성취 증명서

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PyTorch와 함께 텍스트 딥러닝의 흥미로운 여정을 시작해 보세요. 이 강의는 다양한 텍스트 관련 과제를 해결하는 데 필요한 역량을 갖추도록 도와줍니다. 인코딩과 임베딩을 통한 텍스트 처리의 원리를 살펴보고, 텍스트 데이터를 사용해 CNN, RNN, GAN, 사전 학습 모델 등 다양한 모델을 적용해 볼 거예요. 마지막으로 전이 학습 기법, 어텐션 메커니즘, 적대적 공격으로부터 모델을 보호하는 방법 등 고급 주제도 다룹니다. 강의를 마치면 텍스트를 위한 강력한 딥러닝 모델을 구축하는 데 필요한 기술을 갖추게 됩니다.

선수 조건

Intermediate Deep Learning with PyTorch

1

Introduction to Deep Learning for Text with PyTorch

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.

Introduction to preprocessing for text

Word frequency analysis

Preprocessing text

Encoding text data

One-hot encoded book titles

Bag-of-words for book titles

Applying TF-IDF to book descriptions

Introduction to building a text processing pipeline

Shakespearean language preprocessing pipeline

Shakespearean language encoder

2

Text Classification with PyTorch

Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch, and understand how to evaluate your models using suitable metrics.

Overview of Text Classification

Embedding in PyTorch

Categorizing text classification tasks

Convolutional neural networks for text classification

Build a CNN model for text

Train a CNN model for text

Testing the Sentiment Analysis CNN Model

Recurrent neural networks for text classification

Building an RNN model for text

Building an LSTM model for text

Building a GRU model for text

Evaluation metrics for text classification

Evaluating RNN classification models

Evaluating the model's performance

Comparing models

3

Text Generation with PyTorch

Venture into the exciting world of text generation and its applications in NLP. Understand how to leverage Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and pre-trained models for text generation tasks using PyTorch. Alongside, you'll learn to evaluate the performance of your models using relevant metrics.

Introduction to text generation

Creating a RNN model for text generation

Text generation using RNN - Training and Generation

Generative adversarial networks for text generation

Building a generator and discriminator

Training a GAN model

Pre-trained models for text generation

Text completion with pre-trained GPT-2 models

Language translation with pretrained PyTorch model

Evaluation metrics for text generation

Evaluating pretrained text generation model

Understanding text generation metrics

4

Advanced Topics in Deep Learning for Text with PyTorch

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.

Transfer learning for text classification

Transfer learning using BERT

Evaluating the BERT model

Transformers for text processing

Creating a transformer model

Training and testing the Transformer model

Attention mechanisms for text processing

Creating a RNN model with attention

Training and testing the RNN model with attention

Adversarial attacks on text classification models

Adversarial attack classification

Safeguarding AI at PyBooks

PyTorch로 배우는 텍스트 딥러닝

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19백만 명 이상의 학습자와 함께 PyTorch로 배우는 텍스트 딥러닝을(를) 시작하세요!

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계속 진행하시면 당사의 이용약관, 개인정보처리방침 및 귀하의 데이터가 미국에 저장되는 것에 동의하시는 것입니다.

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