Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

In just a few years, large language models (LLMs) have expanded from millions to hundreds of billions of parameters, showcasing the remarkable progress in our ability to engineer and scale massive AI ...

Understand Local Response Normalization (LRN) in deep learning: what it is, why it was introduced, and how it works in convolutional neural networks. This tutorial explains the intuition, mathematical ...

Abstract: Edge caching presents a promising avenue for mitigating backbone network congestion by strategically caching frequently accessed content at the network periphery. As most current edge ...

Abstract: Reinforcement Learning (RL) serves as a fundamental learning paradigm in the field of artificial intelligence, enabling decision-making policies through interactions with environments.

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...

Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation. They have also recently emerged as a promising approach in robotics, especially ...