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Deep Learning for Text with PyTorch

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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4 Hours16 Videos50 Exercises
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Course Description

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.
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In the following Tracks

Deep Learning in Python

Go To Track

Developing Large Language Models

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  1. 1

    Introduction to Deep Learning for Text with PyTorch

    Free

    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.

    Play Chapter Now
    Introduction to preprocessing for text
    50 xp
    Word frequency analysis
    100 xp
    Preprocessing text
    100 xp
    Encoding text data
    50 xp
    One-hot encoded book titles
    100 xp
    Bag-of-words for book titles
    100 xp
    Applying TF-IDF to book descriptions
    100 xp
    Introduction to building a text processing pipeline
    50 xp
    Shakespearean language preprocessing pipeline
    100 xp
    Shakespearean language encoder
    100 xp
  2. 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.

    Play Chapter Now
  3. 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.

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GroupTraining 2 or more people?

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In the following Tracks

Deep Learning in Python

Go To Track

Developing Large Language Models

Go To Track

Datasets

Shakespeare Text

Collaborators

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James Chapman
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Maham Khan
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Jasmin Ludolf
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Chris Harper

Audio Recorded By

Shubham Jain's avatar
Shubham Jain
Shubham Jain HeadshotShubham Jain

Data Scientist

A dynamic and dedicated Artificial Intelligence Researcher and Lecturer, Shubham's expertise lies in Data Science, Machine Learning, Artificial Intelligence, and Software Development applications, skills honed through a rich history of roles in prestigious institutions and companies. Currently, he is pursuing a Ph.D. in Computer Science from the Technological University of Shannon, where he also imparts knowledge as a part-time lecturer, alongside a similar role at the UCD Professional Academy. In the corporate sphere, Shubham has made significant strides, holding the position of Senior Data Scientist at Mastercard and previously contributing as a Senior Researcher at Ericsson. A thought leader in his field, Shubham has presented groundbreaking research in renowned conferences and holds patents in innovative areas of Artificial Intelligence and Machine Learning.
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