Fast Motion-Resolved 4D MRI Using Convolutional Networks Without Data Consistency
Explore cutting-edge techniques for rapid 4D MRI reconstruction using convolutional networks, focusing on motion-resolved imaging without data consistency constraints.
How Neural Networks Learn Features from Data
Uncover the mechanism behind neural networks' feature learning through the average gradient outer product (AGOP). Explore its applications in various architectures and its role in developing interpretable models for scientific use.
Perception and Learning for Autonomous Driving - Part 1
Explore perception and learning for autonomous driving, covering sensor suites, deep neural networks, and object detection techniques in this comprehensive tutorial.
The Deep Learning Revolution
Explore the evolution of deep learning, from its origins to modern applications, covering key milestones, breakthroughs, and future prospects in AI and neuroscience.
Deep Learning II - Joan Bruna NYU
Explore advanced deep learning concepts including symmetry, transformations, and geometric stability in neural networks with NYU professor Joan Bruna.
News on Tensor Networks for Machine Learning and Quantum Computing Simulation
Explore recent advancements in tensor networks for enhancing machine learning and simulating quantum processors, covering deep neural networks, convolutional networks, and large language models.
Machine Duping - Pwning Deep Learning Systems
Explore techniques for exploiting vulnerabilities in deep learning systems, covering neural networks, convolutional networks, and recurrent neural networks, with a focus on practical attack methods.
Machine Learning Exposed - The Fundamentals
Gentle introduction to machine learning fundamentals, covering supervised, unsupervised, and reinforcement learning, with practical examples and intuitive explanations of underlying mathematics.
Writing a Neural Net from Scratch
Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.