Data Science: Transformers for Natural Language Processing
Data Science: Transformers for Natural Language Processing
Data Science: Transformers for Natural Language Processing. BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow
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What you'll learn
- Apply transformers to real-world tasks with just a few lines of code
- Fine-tune transformers on your own datasets with transfer learning
- Sentiment analysis, spam detection, text classification
- NER (named entity recognition), parts-of-speech tagging
- Build your own article spinner for SEO
- Generate believable human-like text
- Neural machine translation and text summarization
- Question-answering (e.g. SQuAD)
- Zero-shot classification
- Understand self-attention and in-depth theory behind transformers
- Implement transformers from scratch
- Use transformers with both Tensorflow and PyTorch
- Understand BERT, GPT, GPT-2, and GPT-3, and where to apply them
- Understand encoder, decoder, and seq2seq architectures
- Master the Hugging Face Python library
Welcome to Data Science: Transformers for Natural Language Processing.
Ever since Transformers arrived on the scene, deep learning hasn't been the same.
Machine learning is able to generate text essentially indistinguishable from that created by humans
We've reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and more
We've created multi-modal (text and image) models that can generate amazing art using only a text prompt
We've solved a longstanding problem in molecular biology known as "protein structure prediction"
In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work.
This is different from most other resources, which only cover the former.
The course is split into 3 major parts:
- Using Transformers
- Fine-Tuning Transformers
- Transformers In-Depth
