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所在平台: Udemy |
课程主页: https://www.udemy.com/course/convolutional-neural-net-cnn-for-developers/
课程评论：没有评论
**课程名称：** 深度学习：面向开发者的卷积神经网络 **课程概述：** 本课程专注于卷积神经网络（CNN）架构，旨在帮助您从基础到进阶，真正掌握深度学习。 * **入门：** 课程将从深度学习的基础概念讲起，深入了解 TensorFlow 和 PyTorch 等深度学习框架的内部工作原理。 * **核心内容：** 重点讲解卷积神经网络的核心概念和实现，包括： * 图像的本质 * 卷积操作的原理 * 如何实现基础的神经网络 * 反向传播的机制 * 迁移学习的应用 * **实践项目：** 通过一系列实际项目，巩固学习成果，并探索深度学习和计算机视觉中的关键概念。 * **编程环境：** 所有示例代码均使用 Python 编写，并集成在带有详细注释的 Jupyter Notebook 中，方便您理解和学习。即使 Python 基础不扎实，也能轻松跟随。 * **GPU 加速：** 高级部分将用到 GPU，但无需担心复杂设置。课程提供一键式运行的 Google Colab 示例，免费且无需配置，只需一个 Google 账号。 * **学习收益：** 完成本课程后，您将深入理解深度学习的前沿创新，并有能力将其应用于您的项目中，提升项目体验。
This course will teach you Deep learning focusing on Convolution Neural Net architectures. It is structured to help you genuinely learn Deep Learning by starting from the basics until advanced concepts. We will begin learning what it is under the hood of Deep learning frameworks like Tensorflow and Pytorch, then move to advanced Deep learning Architecture with Pytorch.During our journey, we will also have projects exploring some critical concepts of Deep learning and computer vision, such as: what is an image; what are convolutions; how to implement a vanilla neural network; how back-propagation works; how to use transfer learning and more.All examples are written in Python and Jupyter notebooks with tons of comments to help you to follow the implementation. Even if you don't know Python well, you will be able to follow the code and learn from the examples.The advanced part of this project will require GPU but don't worry because those examples are ready to run on Google Colab with just one click, no setup required, and it is free! You will only need to have a Google account. By following this course until the end, you will get insights, and you will feel empowered to leverage all recent innovations in the Deep Learning field to improve the experience of your projects.
