|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-for-computer-vision/
课程评论：没有评论
**课程名称：** 计算机视觉中的深度学习：从像素到语义 **课程概述：** 本课程深入探讨计算机视觉领域的深度学习技术。课程分为三个主要部分： **第一部分：传统计算机视觉基础与卷积神经网络（CNN）入门** * **传统计算机视觉流水线：** 介绍图像处理的基本概念，包括图像预处理技术，如阈值处理、去噪、模糊、滤波、边缘检测和轮廓提取。 * **实践应用：** 构建简单的应用程序，例如车牌识别（LPD）和活动识别。 * **深度学习革命：** 介绍深度学习如何革新计算机视觉，将传统滤波器转化为可学习的参数，即卷积神经网络（CNN）。 * **CNN基础：** 详细讲解CNN的基本原理，包括Vanilla架构在图像分类中的应用，以及卷积核、步长、池化层和特征图尺寸计算等超参数。 * **先进CNN架构：** 探讨当前最先进的CNN元架构和设计模式，如Skip Connections、Inception和DenseNet。 **第二部分：CNN的实际应用与调试** * **解决实际问题：** 学习如何利用CNN解决不同场景下的实际问题，特别是在数据量较少的情况下。 * **迁移学习：** 掌握迁移学习的概念及其在不同场景下的应用。 * **调试与可视化：** 学习如何调试CNN模型并可视化学习到的卷积核。 **第三部分：高级计算机视觉应用** * **语义分割：** 学习Encoder-Decoder设计模式，并从零开始构建U-Net架构，用于Cambridge Video (CAMVID) 数据集。 * **目标检测：** 介绍两阶段和单阶段目标检测架构，如SSD和YOLO。 * **视频处理：** 学习如何利用时空卷积神经网络（Spatio-Temporal ConvNet）架构处理视频数据。 * **3D深度学习：** 介绍3D深度学习，将CNN的应用扩展到处理3D数据，如LiDAR数据。
Welcome to our course, Deep Learning for Computer Vision: From Pixels to Semantics. In this course, we will cover three main parts. The first part covers the essentials of traditional computer vision pipeline, and how to deal with images in OpenCV and Pillow libraries, including the image pre-processing pipeline like: thresholding, denoising, blurring, filtering, edge detection, contours...etc. We will build simple apps like Car License Plate Detection (LPD) and activity recogntion. This will lead us to the revolution that deep learning brought to the game of computer vision, turning traditional filters into learnable parameters using Convolution Neural Networks. We will cover all the basics of ConvNets, including the details of the Vanilla architecture for image classification, hyper parameters like kernels, strides, maxpool and feature maps sizes calculations. Beyond the Vanilla architecture, we also cover the state-of-the art ConvNet meta-architectures and design patters, like skip-connnections, Inception, DenseNet...etc. In the second part, we will learn how to use ConvNets to solve practical problems in different situations, with small amount of data, how to use transfer learning and the different scenarios for that, and finally how to debug and visualize the leant kernels in ConvNets. In the last part, we will learn about different CV apps using ConvNets. We will learn about the Encoder-Decoder design pattern. We start by the task of semantic segmentation, where we will build a U-Net architecture from scratch for the Cambridge Video (CAMVID) dataset. Then we will learn about Object Detection, covering both 2-stage and one-shot architectures like SSD and YOLO. Next, we will learn how to deal with the video data using the Spatio-Temporal ConvNet architectures. Finally we will introduce 3D Deep Learning to extend ConvNets usage to deal with 3D data, like LiDAR data.
