computational graph

360 Smart Cloud

Sep 30, 2021 · Artificial Intelligence

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

This article explains how computational graphs can represent arbitrary neural networks, describes forward and reverse propagation, details the implementation of automatic differentiation with Python and NumPy, and demonstrates building and training a multilayer fully‑connected network on the MNIST dataset using custom graph nodes and optimizers.

Automatic DifferentiationDeep LearningPython

0 likes · 29 min read

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

360 Tech Engineering

Aug 28, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP

This article explains how Huajiao Live leverages Spark for data preprocessing and TensorFlow (augmented by the TensorSlow project) for distributed deep‑learning training, detailing computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and a step‑by‑step Python implementation of a multi‑layer perceptron.

Deep LearningMLPPython

0 likes · 14 min read

Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP

360 Tech Engineering

Jul 2, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide

This article explains how TensorFlow powers Huajiao Live's recommendation system, introduces the TensorSlow project for demystifying TensorFlow's core, and walks through deep‑learning fundamentals, computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and building a multi‑layer perceptron with Python code examples.

Deep LearningMLPPython

0 likes · 13 min read

Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide

Huajiao Technology

Jul 2, 2019 · Artificial Intelligence

Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation

This article introduces deep learning applications at Huajiao Live, explains TensorFlow's computational graph architecture, details core concepts such as placeholders, variables, operations, forward and backward propagation, and provides complete Python-like code examples for building and training a multi-layer perceptron.

Deep LearningMLPPython

0 likes · 14 min read

Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation