Deep Multi-Task and Meta Learning

Deep Multi-Task and Meta Learning is an advanced course that explores the latest developments in deep learning and its applications to multi-task and meta-learning problems. The course begins by introducing the fundamental concepts and architectures of deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Students will learn about the different types of layers and activation functions used in deep learning, and how to train and optimize deep neural networks using backpropagation and stochastic gradient descent. The course then delves into multi-task learning, where a single deep neural network is trained to perform multiple tasks simultaneously. Students will learn about the different approaches to multi-task learning, including hard parameter sharing, soft parameter sharing, and cross-stitch networks. They will also learn how to use transfer learning to leverage pre-trained models for multi-task learning. Next, the course covers meta-learning, which is the process of learning to learn. Students will learn about the different types of meta-learning, including model-based and model-free approaches, and how to use meta-learning to improve the performance of deep neural networks on new tasks with limited data. They will also learn about the different algorithms used in meta-learning, including gradient-based and optimization-based methods. Throughout the course, students will work on hands-on projects and assignments that will allow them to apply the concepts and techniques learned in class. They will have the opportunity to work with real-world datasets and apply state-of-the-art deep learning models to solve complex problems. Upon completing the course, students will have a deep understanding of the latest developments in deep multi-task and meta-learning, and how to apply these techniques to real-world problems. They will also have gained hands-on experience with deep learning frameworks such as TensorFlow and PyTorch, and be well-prepared to tackle the most challenging problems in the field of deep learning. Overall, Deep Multi-Task and Meta Learning is a challenging and rewarding course that is ideal for advanced students and professionals looking to expand their knowledge of deep learning and its applications. Author: Chelsea Finn (Stanford University)