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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-image-classification-in-pytorch-20/
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课程名称：深度学习图像分类与 PyTorch 2.0 课程概述： 欢迎参加本课程《深度学习图像分类与 PyTorch 2.0》。如果你希望学习如何建立强大的图像分类识别系统，以高准确率识别对象，那么这个课程就是你所需要的！在这门课程中，你将踏上深度学习和图像分类的精彩旅程。本课程旨在让你掌握构建和训练深度神经网络以进行图像分类的知识和技能，使用 PyTorch 框架。 课程内容包含多个章节，每个章节将介绍一个新的图像分类模型训练概念。主要内容包括： - 使用 PyTorch 2.0 中新功能 torch.compile 训练模型。 - 安装 Cuda 和 Cudnn 库，以便在 PyTorch 2.0 中使用 GPU。 - 如何使用 Google Colab Notebook 编写 Python 代码并逐步执行代码单元。 - 将 Google Colab 连接到 Google Drive，以访问驱动器数据。 - 掌握行业标准的数据准备艺术，包括使用 torchvision 库进行数据处理。 - 通过数据增强生成新的图像分类数据，方法包括：调整大小、裁剪、随机水平翻转、随机垂直翻转、随机旋转和颜色抖动。 - 实现数据管道，与数据加载器高效处理大型数据集。 - 深入了解各种模型架构，如 LeNet、VGG16、Inception v3 和 ResNet50，借助层级图表深入理解每个模型。 - 实现训练和推理管道。 - 理解迁移学习，以便在较少的数据上训练模型。 - 将模型推理结果显示回图像上，以便于可视化。 通过本课程的学习，你将能够设计和构建基于深度学习的图像分类模型，掌握的技能将为你在各行各业解决复杂的图像分析问题打开大门。无论你是初学者还是经验丰富的数据科学家，这门课程都将帮助你在深度学习（计算机视觉）领域取得成功。 如有任何问题，欢迎通过Udemy的问答板与我联系，我们将尽快为您提供最佳回复。感谢查看课程页面，期待在我的课程中见到你！
Welcome to this Deep Learning Image Classification course with PyTorch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for PyTorch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and ColorJitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as LeNet, VGG16, Inception v3, and ResNet50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result back onto the image for visualization purposes. By the end of this comprehensive course, you'll be well-prepared to design and build image classification models using deep learning with PyTorch2.0. These skills will open doors to a wide range of applications, from classifying everyday objects to solving complex image analysis problems in various industries. Whether you're a beginner or an experienced data scientist, this course will equip you with the knowledge and practical experience to excel in the field of deep learning(Computer Vision).Feel Free to message me on the Udemy Ques and Ans board, if you have any queries about this Course. We give you the best reply in the shortest time as soon as possible.Thanks for checking the course Page, and I hope to see you in my course.
