What will you learn in this Natural Language Processing with Attention Models Course
Implement encoder-decoder architectures with attention mechanisms for machine translation tasks.
Build Transformer models for text summarization applications.
Utilize pre-trained models like BERT and T5 for question-answering systems.
Understand and apply concepts such as self-attention, causal attention, and multi-head attention in NLP tasks
Program Overview
1. Neural Machine Translation with Attention
⏳ 7 hours
Explore the limitations of traditional sequence-to-sequence models and learn how attention mechanisms can enhance translation quality. Build a neural machine translation model that translates English sentences into German using attention.
2. Text Summarization with Transformers
⏳ 8 hours
Compare RNNs with Transformer architectures and implement a Transformer model to generate text summaries, understanding components like self-attention and positional encoding.Coursera+1Class Central+1
3. Question Answering with Pre-trained Models
⏳ 11 hours
Delve into transfer learning by leveraging state-of-the-art models such as BERT and T5 to build systems capable of answering questions based on given contexts.
Get certificate
Job Outlook
Equips learners for roles such as NLP Engineer, Machine Learning Engineer, and AI Specialist.
Applicable in industries like technology, healthcare, finance, and e-commerce where language models are integral.
Enhances employability by providing hands-on experience with cutting-edge NLP techniques and tools.
Supports career advancement in fields requiring expertise in deep learning and natural language understanding.
Specification: Natural Language Processing with Attention Models
|
