This tutorial explains how to use OpenCV, pre‑trained deep‑learning models, and Python to automatically detect faces and predict a person's age from static images or real‑time video, covering model selection, project structure, script usage, result analysis, and ways to improve accuracy.
In this candid interview, Alibaba AI veteran Jia Yangqing shares his late start in programming, the creation of Caffe, perspectives on deep learning, AutoML, knowledge graphs, and practical advice for engineers on career growth, research focus, and the challenges of bringing AI to real‑world products.
The AVS Working Group, backed by iQIYI, has launched the Deep Learning Based Video Coding Loop Filter Challenge to spur research on AI‑driven loop filters that can cut bitrate by over 15%, inviting participants to submit Caffe models on the TAVS3 platform with ongoing benchmarking and potential prizes, while encouraging contributions to the upcoming AVS video coding standard.
This article explores how deep learning, specifically convolutional neural networks built with Caffe, can be used to automatically detect and predict static UI compatibility bugs such as text overlap in mobile app front‑ends, reducing reliance on manual testing.
This article compares the most popular open‑source deep‑learning frameworks—including TensorFlow, Caffe, Caffe2, CNTK, MXNet, Torch, PyTorch, Deeplearning4J and Theano—detailing their origins, key features, strengths, weaknesses, ecosystem support, and the trade‑offs between open‑source and proprietary AI solutions.
This article describes how Yahoo integrated deep learning into its massive Hadoop ecosystem by adding GPU nodes, using YARN and Spark to run Caffe at scale, and presents performance results on AlexNet and GoogLeNet alongside open‑source contributions.
