Are you looking to learn about Natural Language Processing (NLP) with Transformers in Python?
If so, this course is for you! In this course, you'll learn about the basics of NLP with Transformers, including how to train your own models and use them to perform tasks such as text classification, entity recognition, and question answering.
You'll also get to grips with popular libraries such as spaCy and Transformers, and learn how to deploy your models in a production environment. By the end of this course, you'll be able to confidently build and deploy your own NLP models with Transformers.
What You'll Learn Natural Language Processing Nlp With Transformers In Python Course
In recent years, there has been a surge of interest in natural language processing (NLP). A key reason for this is the success of deep learning models in a range of NLP tasks. In particular, transformer-based models such as BERT have shown promise in a range of tasks such as text classification, question answering, and machine translation.
In this course, you will learn how to use transformer-based models for NLP tasks. You will start by working through a series of tutorials to get up to speed with the basics of working with transformers. You will then apply these skills to a range of NLP tasks, including text classification, question answering, and machine translation.
By the end of this course, you will be able to confidently use transformer-based models for a range of NLP tasks. You will also have a solid understanding of the key concepts behind these models.
Why You Should Take Natural Language Processing Nlp With Transformers In Python Course
NLP with Transformers in Python is a course that will teach you the basics of Natural Language Processing. You will learn how to use the Transformers library to build models that can process and understand natural language. This course is perfect for anyone who wants to learn about NLP and how to use Python to build NLP models.
The course starts with an introduction to NLP and the Transformers library. You will then learn how to preprocess text data and build a simple NLP model. The course then covers more advanced topics such as building a text classifier and a question-answering system. By the end of the course, you will have a strong understanding of NLP and how to use the Transformers library to build NLP models.
If you're interested in learning about Natural Language Processing, this course is for you. NLP with Transformers in Python will teach you the basics of NLP and how to use the Transformers library to build NLP models.
We cover several key NLP frameworks including:
- HuggingFace's Transformers
- TensorFlow 2
- PyTorch
- spaCy
- NLTK
- Flair
- Language classification/sentiment analysis
- Named entity recognition (NER)
- Question and Answering
- Similarity/comparative learning
We go through a variety of examples to show how transformers are important for each of these use cases. We also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.
This article provides support for several other sections that teach us how to design, implement, and measure the performance of our models more effectively. These sections include:
- History of NLP and where transformers come from
- Common preprocessing techniques for NLP
- The theory behind transformers
- How to fine-tune transformers
We cover all this and more, I look forward to seeing you in the course!
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