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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-for-computer-vision-with-tensorflow-2-2022/
课程评论：没有评论
课程名称：使用Tensorflow 2.X的计算机视觉深度学习 课程概述： 本课程是更新版的《使用Tensorflow 2.X的计算机视觉深度学习》课程，详细讲解了许多最新的图像分类和目标检测算法。课程完全采用Google Colaboratory (Colab)编写，以帮助没有本地GPU的学生顺利学习，当然有GPU的学生也能轻松跟上。课程从卷积神经网络（ConvNets）的基础知识开始，这些是图像分类和最新目标检测算法特征提取器的基础。 主要学习内容包括： - 计算机视觉中的图像基础 - 使用TensorFlow加载图像生成器 - 卷积操作 - 稀疏连接和参数共享 - 深度可分离卷积 - 填充 - 使用TensorFlow的Conv2D层 - 池化层 - 全连接层 - 批量归一化 - ReLU激活及其他函数 - 训练参数数量的计算 - 图像增强等 不同的ConvNets架构，如LeNet5、AlexNet、VGG-16、ResNet、Inception以及最新的视觉变换器(ViT)等。此外，还将进行多种实际应用，如Covid19的X光图像分析、CIFAR10、Fashion MNIST、BCCD、COCO数据集和Open Images Dataset V6等。 在目标检测章节中，将学习目标检测算法的理论及应用，从基础到最新前沿算法，能够开发实用应用。学习内容包括目标检测的里程碑、性能指标、R-CNN系列的理论背景，以及利用Faster R-CNN检测血细胞等实际应用。此外，还将探索YOLO系列算法及其在自定义数据集和应用中的使用，如图像和视频中的对象检测、车牌识别和面罩检测等。 更新后的课程还新增了图像分割章节，涵盖了U-Net的理论，项目包括使用U-Net检测MRI图像中的脑肿瘤。 本课程通过直观的算法概念综述理论，并提供多种实践案例，帮助学生将知识转化为实际应用。课程在前一版本的基础上有了显著改进，受到学生们的高度评价，许多学员的反馈表示课程内容丰富，讲解清晰，是计算机视觉领域非常优秀的学习材料。
This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2.X.It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.The course was entirely written using Google Colaboratory(Colab) in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one.This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms.We're going to study in detail the following concepts and algorithms:- Image Fundamentals in Computer Vision,- Load images in Generators with TensorFlow,- Convolution Operation,- Sparsity Connections and parameter sharing,- Depthwise separable convolution,- Padding,- Conv2D layer with Tensorflow,- Pooling layer,- Fully connected layer,- Batch Normalization,- ReLU activation and other functions,- Number of training parameters calculation,- Image Augmentation, etc- Different ConvNets architectures such as: * LeNet5, * AlexNet, * VGG-16, * ResNet, * Inception, * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as: * Covid19 on X-Ray images, * CIFAR10, * Fashion MNIST, * BCCD, * COCO dataset, * Open Images Dataset V6 through Voxel FiftyOne, * ROBOFLOWIn the Object Detection chapter we'll learn the theory and the application behind the main object detection algorithms doing a journey since the beginnings to the latest state of the art algorithms.You'll be able to use the main algorithms of object detection to develop practical applications.Some of the content in this Chapter is the following:- Object detection milestones since Selective Search algorithm,- Object detection metrics,- Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN,- Detect blood cells using Faster R-CNN application,- Theoretical background for Single Shot Detector (SSD),- Train your customs datasets using different models with TensorFlow Object Detection API- Object Detection on images and videos,- YOLOv2 and YOLOv3 background.- Object detection from COCO dataset application using YOLOv4 model.- YOLOv4 theoretical class- Practical application for detecting Robots using a custom dataset (R2D2 and C3PO robots dataset) and YOLOv4 model- Practical application for License Plate recognition converting the plates images in raw text format (OCR) with Yolov4, OpenCV and ConvNets-Object detection with the latest state of the art YOLOv7.-Face Mask detection application with YOLOv7I have updated the course with a new chapter for Image Segmentation:- I review the theory behind U-Net for image segmentation- We develop an application for detecting brain tumors from MRI images using U-Net.- We train models with U-Net and U-Net with attention mechanism.You will find in this course a concise review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.This new course represents a huge improvement of the previous course, however the previous course was very well qualified by the students, some of the inspiring comments are here:* Maximiliano D'Amico (5 stars): Very interesting and updated course on YOLO!* Stefan Lankester (5 stars): Thanks Carlos for this valuable training. Good explanation with broad treatment of the subject object recognition in images and video. Showing interesting examples and references to the needed resources. Good explanation about which versions of different python packages should be used for successful results.* Shihab (5 stars): It was a really amazing course. Must recommend for everyone.* Estanislau de Sena Filho (5 stars): Excellent course. Excellent explanation. It's the best machine learning course for computer vision. I recommend it* Areej AI Medinah (5 stars): The course is really good for computer vision. It consists of all material required to put computer vision projects in practice. After building a great understanding through theory, it also gives hands-on experience.* Dave Roberto (5 stars): The course is completely worth it. The teacher clearly conveys the concepts and it is clear that he understands them very well (there is not the same feeling with other courses). The schemes he uses are not the usual ones you can see in other courses, but they really help much better to illustrate and understand. I would give eight stars to the course, but the maximum is five. It's one of the few Udemy courses that has left me really satisfied.
