Data Science Transformers for Natural Language Processing Udemy coupons

Data Science: Transformers for Natural Language Processing

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

Created by Lazy Programmers Team, Lazy Programmers Inc. | 12 hours on-demand video course

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.

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

Recommended Course

Natural Language Processing: Machine Learning NLP In Python

Deep Learning A-Z™: Hands-On Artificial Neural Networks [2022 Edition]

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