Data Science: Transformers for Natural Language Processing Course
BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras
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 by Lazy Programmer
Artificial Intelligence: Reinforcement Learning in Python
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Machine Learning: Natural Language Processing in Python (V2)
Financial Analysis: Build a ChatGPT Pairs Trading Bot
Time Series Analysis, Forecasting, and Machine Learning
Math 0-1: Calculus for Data Science & Machine Learning
PyTorch: Deep Learning and Artificial Intelligence
Cluster Analysis and Unsupervised Machine Learning in Python