Deep Learning for Natural Language Processing

Deep learning has revolutionized the field of natural language processing and led to many state-of-the-art results. This course introduces students to neural network models and training algorithms frequently used in natural language processing. At the end of this course, learners will be able to explain and implement feedforward networks, recurrent neural networks, and transformers. They will also have an understanding of transfer learning and the inner workings of large language models. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

• Define feedforward networks, RNNs, attention mechanisms, and transformers
• Implement and train these neural network models
• Explain transfer learning concepts and algorithms
• Design neural architectures for NLP tasks

• Basic familiarity with the topic and terminology
• Willingness to engage in practical exercises or case studies