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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-cnn/
课程评论：没有评论
**课程名称：** 使用 Python 和卷积神经网络 (CNN) 进行图像分类的深度学习 **课程概述：** 本课程将带您从零开始，学习如何使用 Keras 和 TensorFlow 后端构建卷积神经网络 (CNN)，以解决自定义的图像分类问题。您无需高端工作站，全程将在免费的 Google Colab 环境中进行。只需稳定的网络连接和一个免费的 Gmail 账号即可。 这是一门注重实践的课程，我们将专注于 Python 编程，确保您深入理解程序的每一个细节。学成之后，您将能够独立构建和训练基于 Keras 和 TensorFlow 的 CNN 模型，可视化数据，并利用模型对新数据进行预测。 图像分类在各行各业都有广泛的应用，掌握这项技能能为您的项目作品集增添亮点，对求职面试至关重要。本课程设计精炼，旨在高效利用您的学习时间。祝您学习愉快！ **就业前景参考（美国）：** 根据 Talent 的数据，美国图像处理工程师的平均年薪为 125,550 美元，即每小时 64.38 美元。入门级职位的年薪约为 102,500 美元，而经验丰富的从业者最高可达 174,160 美元。
Welcome to the "Deep Learning for Image Classification in Python with CNN" course. In this course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend from scratch, and you will learn to train CNNs to solve custom Image Classification problems. Please note that you don't need a high-powered workstation to learn this course. We will be carrying out the entire project in the Google Colab environment, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. By the end of this course, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to visualise data and use the model to make predictions on new data. This image classification course is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your following job interview. This course is designed most straightforwardly to utilize your time wisely. Happy learning.How much does an Image Processing Engineer make in the USA? (Source: Talent)The average image processing engineer salary in the USA is $125,550 per year or $64.38 per hour. Entry-level positions start at $102,500 per year, while most experienced workers make up to $174,160 per year.
