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Code DAO
Dec 8, 2021 · Artificial Intelligence

Optimizers and Schedulers in Neural Network Architecture: A Detailed Guide

This article explains how optimizers and learning‑rate schedulers work, how to configure their hyperparameters and parameter groups, and how to apply differential learning rates and adaptive schedules in PyTorch and Keras to improve model training and transfer‑learning performance.

KerasPyTorchhyperparameter tuning
0 likes · 10 min read
Optimizers and Schedulers in Neural Network Architecture: A Detailed Guide
Code DAO
Code DAO
Dec 6, 2021 · Artificial Intelligence

Why So Many Optimizers? Core Algorithms Behind Neural Network Training

This article explains the fundamental gradient‑descent optimizers used in neural networks—SGD, Momentum, RMSProp, Adam and their variants—illustrates loss‑surface challenges such as local minima, saddle points and ravines, and shows how techniques like mini‑batching, momentum, adaptive learning rates and scheduling address these issues.

AdamDeep LearningMomentum
0 likes · 11 min read
Why So Many Optimizers? Core Algorithms Behind Neural Network Training