convolutional neural network

Rare Earth Juejin Tech Community

Aug 6, 2023 · Artificial Intelligence

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

This article introduces the principles of image recognition, compares traditional logistic regression with convolutional neural networks, demonstrates their implementation using Python code, visualizes model weights, and explains key concepts such as padding, convolution, pooling, receptive fields, and multi‑layer feature extraction.

Image Recognitionconvolutional neural networkexplainable AI

0 likes · 12 min read

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

DeWu Technology

Jul 18, 2021 · Artificial Intelligence

Deep Learning Techniques for Sentiment Analysis

The article explains how deep‑learning models, particularly convolutional neural networks with token‑level padding, kernel size three, and max‑pooling, can automatically classify e‑commerce product reviews into eight sentiment categories, offering scalable insight for decision‑making and paving the way for recommendation, QA, and risk‑assessment applications.

Natural Language Processingconvolutional neural networkdeep learning

0 likes · 9 min read

Deep Learning Techniques for Sentiment Analysis

Tencent Cloud Developer

Oct 12, 2018 · Artificial Intelligence

Understanding Convolutional Neural Networks (CNN) with Keras

The article introduces convolutional neural networks, explains core concepts such as convolution, padding, stride, and pooling, demonstrates how to calculate output dimensions, and provides a step‑by‑step Keras example that builds, compiles, and trains a multi‑layer CNN for image classification.

CNNKerasPython

0 likes · 8 min read

Understanding Convolutional Neural Networks (CNN) with Keras

Tencent Cloud Developer

Mar 21, 2018 · Artificial Intelligence

Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach

The article proposes a hybrid approach that first filters blacklist words and then classifies suspicious comments with a character-level TextCNN, achieving around 89% precision and 87% recall, demonstrating that simple convolutional networks outperform keyword filters and RNNs for short, noisy abusive Chinese text.

Abusive Comment DetectionNLPText Classification

0 likes · 10 min read

Abusive Comment Detection Using TextCNN: A Strategy + Algorithm Approach