The course covers key topics such as word representation and syntactic processing, helping you design advanced deep-learning models. Using the PyTorch framework, you'll implement and understand various NLP neural network models. You'll explore word vectors like Word2Vec, SVD, and GloVe to represent word meanings and discover semantic relationships through dependency parsing.
The course also includes making accurate word predictions using language models, recurrent neural networks (RNNs), and neural machine translation. Additionally, you'll learn to enhance your NLP models by pretraining transformers for efficient language processing and comprehension.
💲 Pay to Audit
🕒 Approx. 6 Weeks
🧠 Advanced Level
🧾 Paid Certificate Available Upon Completion
🎓 Offered by Stanford
