Data Science: Transformers for Natural Language Processing

ChatGPT, GPT-4, BERT, Deep Learning, Machine Learning & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch

4.77 (2821 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course Gallery

Data Science: Transformers for Natural Language Processing – Screenshot 1 — Screenshot 1 – Data Science: Transformers for Natural Language Processing

Data Science: Transformers for Natural Language Processing – Screenshot 2 — Screenshot 2 – Data Science: Transformers for Natural Language Processing

Data Science: Transformers for Natural Language Processing – Screenshot 3 — Screenshot 3 – Data Science: Transformers for Natural Language Processing

Data Science: Transformers for Natural Language Processing – Screenshot 4 — Screenshot 4 – Data Science: Transformers for Natural Language Processing

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Comidoc Review

Our Verdict

This course offers an in-depth look at Transformer-based approaches with practical application using Hugging Face libraries. It caters to various skill levels, including beginners, by providing a comprehensive exploration of the underlying architectures while implementing them from scratch. While some notebooks and explanations may require polishing for clarity, the course's well-structured progression of topics makes it an engaging journey into transformer concepts for data scientists.

What We Liked

Covers modern Transformer-based approaches using Hugging Face libraries, providing a practical application of Transformers
In-depth explanation of the underlying transformer architecture by implementing it from scratch
Well-structured course with real-world use-cases and comprehensive content suitable for both beginners and experts
Detailed coverage of popular LLMs such as GPT3, 4 and chatGPT

Potential Drawbacks

Notebooks could be more polished, with less manual work required to make them useful for future reference
Subtitles can sometimes lack accuracy, affecting clarity for non-native English speakers
Some explanations and presentations might benefit from simplified language and sequential breakdown of concepts