Introduction to Transformer for NLP with Python
Learn BERT, GPT, and deep learning for NLP! Master machine learning with TensorFlow, PyTorch, Keras & Hugging Face. Explore attention mechanisms in Python.
Course Content
Introduction to Transformer for NLP with Python is an intermediate-level Udemy course taught by Hoang Quy La. It runs 7h 2m, is taught in English, and includes a certificate of completion. Learners rate it 4.4 out of 5 from 39 reviews, with 250 students enrolled. It is a paid course, last updated January 2023.
Unlock the power of Natural Language Processing (NLP) with this comprehensive course! Discover how deep learning and the revolutionary Transformers are transforming the field, enabling machines to generate text remarkably similar to human writing.
This course equips you with practical skills to apply cutting-edge NLP techniques and delve into the underlying theory of Transformers and attention mechanisms. You'll gain expertise in state-of-the-art NLP tasks, including machine translation, question answering, entailment, and named entity recognition (NER).
What makes this course unique?
- Complete NLP Foundation: We cover all the essential natural language processing techniques, providing a solid understanding of the field.
- Focus on Popular Models: Master GPT-2, NER, and BERT, some of the most widely used models in modern NLP.
- Hands-on Projects: Reinforce your learning with numerous practice projects, complete with detailed, step-by-step notebooks for easy reference.
Course Breakdown:
- Part 1: Using Transformers
- Fundamentals of NLP: Understand core concepts like what is natural language processing (NLP), stemming, lemmatization, chunking, and bag of words.
- Practical Projects: Build three engaging projects: gender identification, sentiment analysis, and topic modeling.
- Part 2: Fundamental Transformers
- Transformer Architecture: Explore how transformers function and the innovative Hugging Face Transformers library.
- Project Implementation: Implement projects including an IMDB project and a Q&A project.
- Part 3: Text Generation with GPT-2
- GPT-2 Text Generation: Generate realistic text using GPT-2, solidifying your understanding of transformer capabilities.
- Practical Application: See how quickly and effectively transformers can generate text.
- Part 4: Text Classification & NER
- Text Classification with Transformers: Learn to classify text using transformer models.
- Named Entity Recognition (NER): Explore NER, a key application of transformers.
- Advanced Q&A Project: Complete an advanced Q&A project to further hone your skills.
This course is perfect for anyone looking to enter the exciting world of NLP and leverage the power of transformers for real-world applications.
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