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Deep Learning for Text with PyTorch

Avancé

Updated 12/2024

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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PyTorchIntelligence artificielle4 heures16 vidéos50 exercices4,050 XP4,056Déclaration de réalisation

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Description du cours

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.

Conditions préalables

Intermediate Deep Learning with PyTorch

1

Introduction to Deep Learning for Text with PyTorch

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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

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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

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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

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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

Deep Learning for Text with PyTorch

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