Deep Learning for Text with PyTorch

Overview

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

Embark on an exciting journey into deep learning for text with PyTorch. This course will equip you with the skills to tackle various text-related challenges. You'll dive into the principles of text processing with encoding and embedding. You’ll apply various models, including CNNs, RNNs, GANs, and pre-trained models, using text data. Finally, you'll delve into advanced topics, including transfer learning techniques, attention mechanisms, and how to protect your models from adversarial attacks. By the end of this course, you'll have the skills to build powerful deep learning models for text.

Syllabus

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.

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.

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.

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.