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所在平台: Udemy |
课程主页: https://www.udemy.com/course/convolutional-neural-networks-for-medicine/
课程评论：没有评论
## 卷积神经网络医学应用课程总结 **课程概览** 本课程面向具备 Python 中级水平、卷积神经网络基础知识以及 TensorFlow 基本知识的学习者。课程旨在帮助您掌握如何训练高精度的卷积神经网络，以预测二分类测试图像。学成之后，您将能够独立应用自己的方法进行模型训练，并了解合适的参数选择和数据增强技术。 **核心学习内容** * **二分类图像预测：** 学习训练精确的卷积神经网络模型，用于对测试图像进行二分类预测。 * **多分类图像预测：** 掌握训练和预测多分类卷积神经网络的方法。 * **模型训练与参数调优：** 了解如何选择合适的参数以及使用数据增强技术来提升模型性能。 * **OpenCV (CV2) 应用：** 学会使用 CV2 库进行图像预测。 * **Keras 模型加载：** 掌握 Keras 的 `load_model` 函数，用于加载和应用训练好的二分类及多分类模型。 * **处理小数据集挑战：** 学习应对深度学习中小数据集规模带来的挑战。 * **防止过拟合与减轻偏差：** 掌握有效的技术来防止模型过拟合，并显著降低模型偏差。 **课程特点** * **视频讲解：** 视频内容简洁明了，讲解深入透彻。 * **实践导向：** 课程中所有数据集（除一例外）均来自 Kaggle，注重实际操作。 * **易于掌握：** 即使课程内容涵盖先进技术，讲解方式也力求简单易懂，有助于快速掌握。 * **评估支持：** 只要认真学习视频内容，轻松应对课程测验。 **前提条件** * Python 中级水平 * 卷积神经网络基础知识 * TensorFlow 基本知识 **课程目标** 完成本课程后，您将能够独立开发和训练卷积神经网络模型，用于医学图像的二分类和多分类任务，并能对其进行有效的调优和预测。您也将具备应对实际深度学习项目中常见挑战的能力。
Before starting this course you must at least have an intermediate level of python, basic understanding of convolutional neural networks, and basic knowledge of Tensorflow. By the end of this course you will learn how to train very accurate convolutional neural networks to predict test images for binary class. You know enough to where if you want to go off on your own and use your own methods how to do that. Also appropriate parameters to use as well as data augmentation methods. It is explained in this course how to train multiclass as well. Not to mention you will learn how to use CV2 when predicting an image after training the convolutional neural network. You will also learn how to train a multi class Convolutional Neural Network and predict as well. Then learn to use a Keras Load Model Function for both binary and multi class predictions. Although the videos are short they are thoroughly and simply explained. You will also learn to deal with some of the challenges in deep learning as well when it comes to small dataset size. All the datasets featured in this video are found on Kaggle, except one that I provide to you directly. I will explain why in that video. Do not worry about the quizzes if you pay attention you will easily do great. But most importantly be ready to learn. This is not is challenging as it seems. I show you how to prevent overfitting and reduce bias severely with these methods in these videos.
