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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-with-pytorch-for-medical-image-analysis/
课程评论：没有评论
**课程名称：** 使用PyTorch进行深度学习的医学图像分析 **课程概述：** 您是否渴望将深度神经网络应用于比MNIST、CIFAR10或猫狗等数据集更复杂的任务？您在分割CT图像中的癌症时，是否也想学习最先进的机器学习框架？那么，这门课程将是您的不二之选！ 欢迎来到医学图像深度学习领域最全面的课程之一！本课程专注于将最先进的深度学习架构应用于各种医学图像分析挑战。您将学习如何处理多种任务，包括癌症分割、肺炎分类、心脏检测、模型可解释性等。 **课程内容涵盖：** * NumPy * 机器学习理论 * 测试/训练/验证数据划分 * 模型评估 (回归与分类任务) * PyTorch张量 * 卷积神经网络 (CNNs) * 医学图像分析 * 网络决策的可解释性 (探究网络的决策依据) * 最先进的高层PyTorch库：PyTorch-Lightning * 肿瘤分割 * 三维数据处理 * 以及更多... **为何选择本课程？** 本课程提供独特的机会，让您学习将深度学习应用于高度复杂且非标准化的 (医学) 问题 (涵盖2D和3D)。所有课程单元都包含清晰总结的理论知识和代码实践示例，确保您能理解和跟进每一个步骤。此外，您还可以受益于强大的在线社区，包括拥有数千名学生和专业助教的问答论坛，以及在Discord服务器上的学生互动。 通过本课程，您将掌握绝大多数人工智能工程师所不具备的技能和技术！ **授课讲师：** Jose, Marcel, Sergios & Tobias
Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?Then this is the right course for you!Welcome to one of the most comprehensive courses on Deep Learning in medical imaging!This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.The following topics are covered:NumPyMachine Learning TheoryTest/Train/Validation Data SplitsModel Evaluation - Regression and Classification TasksTensors with PyTorchConvolutional Neural NetworksMedical ImagingInterpretability of a network's decision - Why does the network do what it does?A state of the art high level pytorch library: pytorch-lightningTumor SegmentationThree-dimensional dataand many moreWhy choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.You will learn skills and techniques that the vast majority of AI engineers do not have!-------Jose, Marcel, Sergios & Tobias
