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所在平台: Udemy |
课程主页: https://www.udemy.com/course/la-hoang-quy-complete-convolutional-neural-network-with-python-2022/
课程评论：没有评论
《Python卷积神经网络实战2022》是关于卷积神经网络（CNN）最全面的课程之一，旨在帮助学员从零开始掌握CNN技术，特别关注其他课程中较少涵盖的进阶内容。课程采用高度实践的方式，包含大量项目，有助于学员建立作品集。 课程共分为五个主要部分： **第一部分：卷积神经网络基础** 本部分将介绍CNN的基本概念，是课程的入门阶段。学员将了解CNN的构成，使用的库，并通过详细的步骤分解，学习如何构建一个简单的CNN模型。 **第二部分：CIFAR-10 项目** 学员将学习如何将已掌握的CNN知识应用于大型图像数据集。此项目旨在巩固所学内容，并学习如何整合零散知识来构建处理大规模图像数据的模型，重点在于图像处理的应用。 **第三部分：服装图像项目** 这是另一个实践项目，与CIFAR-10项目类似，提供详细的步骤指导，让学员亲手操作。 **第四部分：CNN高级实现** 本部分将深入探讨其他课程中未涵盖的CNN高级工具和库，如VGG、Inception网络和Deep Dream网络。学员将通过“组合两个图像”的项目，实际实现这些先进网络，并学习如何优化模型结果。 **第五部分：OpenCV、Mask R-CNN、Faster R-CNN 和 YOLO 简介** 本部分继续介绍CNN领域的前沿工具和库，包括OpenCV、Mask R-CNN和Faster R-CNN。学员将了解这些工具的作用和重要性，并通过编码活动实际实现Faster R-CNN、Mask R-CNN和YOLO。
Interested in image processing? Then this course is for you!This is currently the most comprehensive course in the market about convolutional neural networks. The course will guide you from zero to hero on a convolutional neural network which is mostly not covered in any other courses.This course is built in a very practical way as there are lots of projects for you to practice along the way. So you will have lots of projects in your portfolio to show to your potential employers or clientsThe course is split into 4 major parts:Convolutional Neural Network fundamentalCIFAR-10 projectClothing image projectAdvanced implementation of CNNPART 1: Convolutional Neural network fundamentalIn this section, you will learn about the fundamental of the convolutional neural network. This is the first section so there will not be any advanced concept about CNN. This is just an introduction to what a convolutional neural network looks like, and what libraries we will be using. We will also implement a simple CNN model so you will learn how to build it with a detailed explanation step-by-stepPART 2: CIFAR-10 projectIn this section, you will apply what will we have learned so far in the course to build a model for big dataset images. A convolution neural network is mostly used for image processing. This project will help us to reinforce what we have learned so far in the course. Furthermore, it will help us to combine the knowledge together to build a model for the big dataset.PART 3: Clothing image projectThis is another project for you to practice. Similar to the CIFAR-10 project, this project will have you hands-on practice with detailed explanations step-by-step.PART 4: Advanced implementation of CNN.In this section, we will learn some of the advanced tools and libraries in CNN which are not covered in any other courses. VGG, Inception network and the deep dream network will be introduced in this section. We will also implement VGG, Inception network, and the deep dream network in the project "combining two images". Furthermore we will also learn how to improve the result in this section.PART 5: Introduction to OpenCV, Mask R-CNN, Faster R-CNN and YOLO.In this section, we will learn some of the advanced tools and libraries in CNN which are not covered in any other courses. OpenCV, Mask R-CNN and the Faster R-CNN will be introduced in this section. We will also learn what these tools are and why we need to use them. We will also implement Faster R-CNN, Mask R-CNN and YOLO by doing coding activities.
