Tag Archives: Adam Optimizer
Optimization Techniques for Deep Learning: Enhancing Performance and Efficiency
Introduction Training deep neural networks presents several challenges related to memory constraints, computational resources, and convergence issues. This document explores advanced techniques that address these challenges, including optimization algorithms like Stochastic Gradient Descent (SGD), SGD with Momentum, Adam, LARS, and LAMB, as well as methods such as gradient accumulation and activation checkpointing. Optimizing the Loss…
Demystifying Neural Networks: Architectures, Implementations, and Applications
Introduction Overview of Neural Networks Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes, or neurons, organized in layers. These neurons process and transmit information, allowing neural networks to learn patterns and relationships within data. Importance and Applications Neural networks have become fundamental to…