课程

Deep Learning for Text with PyTorch

高级技能水平

更新时间 2026年1月

Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.

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PyTorchArtificial Intelligence4 小时16 视频50 练习4,050 经验值9,592成就声明

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课程描述

Learn Text Processing Techniques

You'll dive into the fundamental principles of text processing, learning how to preprocess and encode text data for deep learning models. You'll explore techniques such as tokenization, stemming, lemmatization, and encoding methods like one-hot encoding, Bag-of-Words, and TF-IDF, using them with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification.

Get Creative with Text Generation and RNNs

The journey continues as you learn how Recurrent Neural Networks (RNNs) enable text generation and explore the fascinating world of Generative Adversarial Networks (GANs) for text generation. Additionally, you'll discover pre-trained models that can generate text with fluency and creativity.

Build Powerful Models for Text Classification

Finally, you'll delve into advanced topics in deep learning for text, including transfer learning techniques for text classification and leveraging the power of pre-trained models. You'll learn about Transformer architecture and the attention mechanism and understand their application in text processing. By the end of this course, you'll have gained practical experience and the skills to handle complex text data and build powerful deep learning models.

先决条件

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

50 经验值

Word frequency analysis

100 经验值

Preprocessing text

100 经验值

Encoding text data

50 经验值

One-hot encoded book titles

100 经验值

Bag-of-words for book titles

100 经验值

Applying TF-IDF to book descriptions

100 经验值

Introduction to building a text processing pipeline

50 经验值

Shakespearean language preprocessing pipeline

100 经验值

Shakespearean language encoder

100 经验值

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

50 经验值

Embedding in PyTorch

100 经验值

Categorizing text classification tasks

100 经验值

Convolutional neural networks for text classification

50 经验值

Build a CNN model for text

100 经验值

Train a CNN model for text

100 经验值

Testing the Sentiment Analysis CNN Model

100 经验值

Recurrent neural networks for text classification

50 经验值

Building an RNN model for text

100 经验值

Building an LSTM model for text

100 经验值

Building a GRU model for text

100 经验值

Evaluation metrics for text classification

50 经验值

Evaluating RNN classification models

100 经验值

Evaluating the model's performance

100 经验值

Comparing models

50 经验值

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

50 经验值

Creating a RNN model for text generation

100 经验值

Text generation using RNN - Training and Generation

100 经验值

Generative adversarial networks for text generation

50 经验值

Building a generator and discriminator

100 经验值

Training a GAN model

100 经验值

Pre-trained models for text generation

50 经验值

Text completion with pre-trained GPT-2 models

100 经验值

Language translation with pretrained PyTorch model

100 经验值

Evaluation metrics for text generation

50 经验值

Evaluating pretrained text generation model

100 经验值

Understanding text generation metrics

50 经验值

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

50 经验值

Transfer learning using BERT

100 经验值

Evaluating the BERT model

100 经验值

Transformers for text processing

50 经验值

Creating a transformer model

100 经验值

Training and testing the Transformer model

100 经验值

Attention mechanisms for text processing

50 经验值

Creating a RNN model with attention

100 经验值

Training and testing the RNN model with attention

100 经验值

Adversarial attacks on text classification models

50 经验值

Adversarial attack classification

100 经验值

Safeguarding AI at PyBooks

50 经验值

50 经验值

Deep Learning for Text with PyTorch

课程完成

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