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360 Tech Engineering
360 Tech Engineering
Sep 16, 2019 · Artificial Intelligence

Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation

This article explains the backpropagation training algorithm for fully connected artificial neural networks, detailing its gradient‑descent basis, mathematical derivation, matrix formulation, and provides a complete Python implementation with mini‑batch stochastic gradient descent, momentum, learning‑rate decay, and experimental results.

PythonSGDbackpropagation
0 likes · 14 min read
Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation
Hulu Beijing
Hulu Beijing
Jan 4, 2018 · Artificial Intelligence

Why SGD Fails and How Momentum, AdaGrad, and Adam Fix It

This article explains why vanilla Stochastic Gradient Descent often struggles in deep learning, describes the challenges of valleys and saddle points, and introduces three major SGD variants—Momentum, AdaGrad, and Adam—detailing their motivations, update rules, and advantages.

AdaGradAdamMomentum
0 likes · 13 min read
Why SGD Fails and How Momentum, AdaGrad, and Adam Fix It
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Aug 21, 2015 · Artificial Intelligence

Facebook’s Distributed Recommendation System: Architecture, Algorithms, and Performance

The article explains how Facebook built a large‑scale distributed recommendation system using Apache Giraph, collaborative filtering with matrix factorization, SGD and ALS algorithms, a novel work‑to‑work communication scheme, and performance optimizations that achieve ten‑fold speedups on billions of ratings.

ALSApache GiraphFacebook
0 likes · 9 min read
Facebook’s Distributed Recommendation System: Architecture, Algorithms, and Performance