Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

DataCamp

Deep Learning for Text with PyTorch

via

Overview

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

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.

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.

Taught by

Shubham Jain

Reviews

Start your review of Deep Learning for Text with PyTorch

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.