Natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. The field of NLP is evolving rapidly as new methods and toolsets converge with an ever-expanding availability of data. In this course, you will explore the fundamental concepts of NLP and its role in current and cutting-edge research on Large Language Models (LLMs).

You will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, you will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models for question answering, machine translation, and other language understanding tasks.

• Design, implement, and understand your NLP neural network models, using the Pytorch framework.
• Represent word meaning with word vectors, such as Word2Vec, SVD and GloVe.
• Identify semantic relationships between words in a sentence with dependency parsing.
• Make large scale word predictions with language models, recurrent neural networks (RNNs), and neural machine translation.
• Improve your NLP models and pretrain your transformers for more efficient natural language processing and understanding.

Competency Areas

• Dependency Parsing
• Neural Machine Translation and Attention
• Neural Networks
• RNNs and Language Models
• Transformers and Pretraining
• Using PyTorch From Scratch
• Word Vectors

What You Need to Get Started

Prior to enrolling in your first course in the AI Professional Program, you must complete a short application (15 min) to demonstrate:

• Proficiency in Python: Coding assignments will be in Python. Some assignments will require familiarity with basic Linux command line workflows.
• College Calculus and Linear Algebra: You should be comfortable taking (multivariable) derivatives and understand matrix/vector notation and operations.
• Probability Theory: You should be familiar with basic probability distributions (Continuous, Gaussian, Bernoulli, etc.) and be able to define concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.

Note: Assignments in this course primarily use PyTorch, so prior experience is recommended, along with a basic understanding of machine learning concepts.