Introduction Deep Reinforcement Learning (DRL) is a subset of machine learning where an agent learns to make decisions by interacting with its environment, receiving feedback through rewards or penalties, and optimizing its actions to maximize long-term rewards. In robotics, DRL has shown tremendous potential in enabling machines to learn complex tasks autonomously, with minimal human…
Introduction In deep learning, data is often represented in multidimensional structures known as tensors. These high-dimensional data structures arise in various applications, including computer vision, natural language processing, and recommendation systems. Tensor decomposition is a powerful mathematical tool used to break down these high-dimensional tensors into lower-dimensional components, facilitating better analysis and efficient computations. At…
Introduction In deep learning, one of the most important hyperparameters that significantly affects the performance and convergence of a model is the learning rate. Choosing the right learning rate is critical; if it’s too high, the model may overshoot the optimal solution, and if it’s too low, training can be slow and stuck in suboptimal…