1 - 10 of 12 results for: CS224N

Since their introduction in 2017, Transformers have revolutionized Natural Language Processing (NLP). Now, Transformers are finding applications all over Deep Learning, be it Computer Vision (CV), Reinforcement Learning (RL), Generative Adversarial Networks (GANs), Speech, or even Biology. Among other things, Transformers have enabled the creation of powerful language models like ChatGPT and GPT-4 and significantly elevated the capabilities and impact of artificial intelligence. In this seminar, we examine the details of how Transformers work, and dive deep into the different kinds of Transformers and how they're applied in various fields, with a focus on LLMs. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. We will invite people at the forefront of Transformers research across different domains for guest lectures. Prerequisites: Basic knowledge of Deep Learning (should understand attention) or CS224N/ CS231N/ CS230. Please enroll for the course on Axess.

Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing.

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.

Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems. Prerequisites: CS124, CS221, CS224N, or CS229.

The goal of this practicum is to integrate methods from natural language processing with social psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing. Prerequisite: CS224N, PSYCH290, or equivalent background in natural language processing. Students interested in participating should complete the online application for permission at https://web.stanford.edu/class/cs329r/. Limited enrollment.

This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. Students will learn about data management, data engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects. In the process, students will learn about important issues including privacy, fairness, and security. Pre-requisites: At least one of the following; CS229, CS230, CS231N, CS224N or equivalent. Students should have a good understanding of machine learning algorithms and should be familiar with at least one framework such as TensorFlow, PyTorch, JAX.

Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing. Prerequisite: CS224N or CS224U, or equivalent background in natural language processing.

Deep learning-based AI systems have demonstrated remarkable learning capabilities. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAccT) of a model in addition to its performance. Although FAccT will be difficult to achieve, emerging technical approaches in this topic show promise in making better FAccT AI systems. In this course, we will study the rigorous computer science necessary foundations for FAccT deep learning and dive into the technical underpinnings of topics including fairness, robustness, interpretability, accountability, and privacy. These topics reflect state-of-the-art research in FAccT, are socially important, and they have strong industrial interest due to government and other policy regulation. This course will focus on the algorithmic and statistical methods needed to approach FAccT AI from a deep learning perspective. We will also discuss several application areas where we can apply these techniques. Prerequisites: Intermediate knowledge of statistics, machine learning, and AI. Qualified students will have taken any one of the following, or their advanced equivalents: CS224N, CS230, CS231N, CS236, CS273B. Alternatively, students who have taken CS229 or have equivalent knowledge can be admitted with the permission of the instructors.

The goal of this practicum is to integrate methods from natural language processing with social psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing. Prerequisite: CS224N, PSYCH290, or equivalent background in natural language processing. Students interested in participating should complete the online application for permission at https://web.stanford.edu/class/cs329r/. Limited enrollment.