Today, I will share the improved model DC-UNet, which is based on the U-Net architecture. The improvement comes from the 2020 paper titled “DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation.” By understanding the concept of this model, similar improvements can be made based on VNet. 1. Original UNet … Read more
Abstract In recent years, deep learning has become a hot research topic. Although it can yield unexpectedly positive results in some cases, bugs within deep learning software can lead to catastrophic consequences, especially when the software is used in safety-critical applications. In this paper, we present an empirical study of a typical deep learning library—TensorFlow—analyzing … Read more
Click on the above “3D Vision Workshop“, select “Star” Delivering valuable content promptly Author | Pan Duoduo Source | Deep Learning and Computer Vision Introduction to 3D-CNN The MNIST dataset classification is considered the hello world program in the field of computer vision. The MNIST dataset helps beginners understand the concept and implementation of Convolutional … Read more
Author | Jeff Hale Translator | Jackey Editor | Jane Produced by | AI Technology Camp (id: rgznai100) 【Introduction】 After the release of TensorFlow 2.0 and PyTorch 1.0, the discussion about which of the two is superior has been ongoing and still lacks a definitive conclusion. In this article, the author analyzes data from multiple … Read more
Source: ZHUAN ZHI This article introduces a book and suggests a 5-minute read. After reading this book, you will understand the mathematical foundations and concepts of deep learning, and be able to build new deep learning applications using the demonstrated prototypes. This book is built on the foundation of the first edition, updating chapters and … Read more
Overview Understand the state-of-the-art transformer models. Learn how we implement transformers for the image captioning problem we have seen using TensorFlow. Compare the results of transformers with attention models. Introduction We have seen that the attention mechanism has become a compelling component of various tasks (such as image captioning) in sequence modeling and transduction models, … Read more
Authorized by AI Technology Camp (ID: rgznai100) Author: Liu Ziying This article is about 1300 words, recommended reading 6 minutes. This article teaches you how to develop deep learning models using 5 steps + 4 basic elements + 9 basic layer structures. As a programmer, we can learn deep learning model development just like learning … Read more
Introduction Creating precise machine learning models capable of identifying and locating multiple objects in a single image remains a core challenge in computer vision. However, with recent advancements in deep learning, object detection applications are easier to develop than ever before. The TensorFlow Object Detection API is an open-source framework built on top of TensorFlow … Read more
What is Scikit-learn? Scikit-learn is an open-source Python library that includes various unsupervised and supervised learning techniques. It is built on technologies and libraries such as Matplotlib, Pandas, and NumPy, which help simplify coding tasks. Features of Scikit-learn include: Classification (including K-Nearest Neighbors) Preprocessing (including Min-Max normalization) Clustering (including K-Means++ and K-Means) Regression (including Logistic … Read more
Click the above“Mechanical and Electronic Engineering Technology” to follow us To install TensorFlow in PyCharm on Windows 10 (for Python 3.9 version), you can follow the steps below: Open PyCharm and create a new Python project or open an existing project. In the top menu bar of PyCharm, select “File” -> “Settings”. In the pop-up … Read more