What you will learn
Build and train Recurrent Neural Networks (RNNs) and their variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
Apply RNNs to character-level language modeling and sequence generation tasks.
Understand and implement word embeddings for natural language processing (NLP) applications.
Utilize Hugging Face tokenizers and transformer models to perform tasks like Named Entity Recognition (NER) and Question Answering.
Program Overview
Recurrent Neural Networks
⏱️11 hours
- Introduction to RNNs and their architectures, including LSTMs and GRUs.
- Understanding backpropagation through time and addressing vanishing gradients.
Natural Language Processing & Word Embeddings
⏱️9 hours
- Learning about word embeddings and their role in NLP.
- Implementing word2vec and GloVe models.
Sequence Models & Attention Mechanism
⏱️ 9 hours
- Exploring sequence-to-sequence models and the attention mechanism.
- Applying these models to machine translation tasks.
Transformer Models & Hugging Face
⏱️ 8 hours
- Understanding transformer architectures and their advantages over RNNs.
- Utilizing Hugging Face libraries for advanced NLP tasks.
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Job Outlook
Proficiency in sequence models is essential for roles such as NLP Engineer, Machine Learning Engineer, and Data Scientist.
Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
Completing this course can enhance your qualifications for positions that require expertise in deep learning and NLP.
Specification: Sequence Models
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