All current Backpropagation Courses courses in 2024
8 Courses
小白学人工智能
中国也在大力发展新一代人工智能技术，并致力于将其应用于各行各业。本课程完成后，学生将能够：
了解人工智能行业的最新应用和发展趋势。
从数据、算法和计算力的角度理解人工智能的发展。
用行业或生活术语比喻人工智能的概念和原理。
体验和理解深度学习原理，涉及CNN、图像风格迁移、RNN等架构。
通过实例理解深度学习特征，如输入层、隐藏层、输.
深度学习
本课程主要面向计科、人工智能及物联网专业的本科生，讲述深度学习基本概念、经典深度学习模型及其实践，主要内容包括前馈神经网络、深度模型优化与正则化、卷积神经网络、循环神经网络等，并介绍深度学习框架的编码实现和参数优化方法。本课程注重理论学习与实践应用的结合，除了课堂讲授之外，还将通过实践环节引导学生使用深度学习平台或工具，让学生通过实际应用来加深.
Deep Learning with TensorFlow 2.0
Advance your technical skills with our specialised course on Deep Learning with TensorFlow 2.0. This course offers a comprehensive guide to building sophisticated deep learning algorithms, immersing you in the world of neural networks.
Learn from Udemy's seasoned instructors through hands-on projects and real-world business case applications..
Image Recognition with Neural Networks From Scratch(FREE)
Unlock the secrets of image recognition by joining our comprehensive, free course titled "Image Recognition with Neural Networks From Scratch" offered by Udemy. Delve deep into the world of Neural Networks and Backpropagation while mastering Python to develop cutting-edge applications in Image Recognition.
This course falls under multiple ca.
人工智能与医学数据计算
《人工智能与医学数据计算》课程共分为十节课。课程从人工智能与医学数据计算的背景知识入手，提供人工智能和深度学习的发展概述。第二课分析人工智能目标，介绍关键技术及相关概念，重点解析两种不同人工智能技术的区别与联系。
第三和第四节课概述人工智能的基本应用场景及操作环境的软硬件要求，为后续的深度学习关键技术学习奠定基础。第五节课关注深度学习网络架构，.
神经网络理论及应用
Course Objective
Since the mid-1980s, artificial neural networks have rapidly evolved into a forefront research field in information technology. This parallel information processing system significantly impacts fields such as computer science, AI, cognitive science, neuroscience, information science, automatic control, and robotics. The course "N.
人工智能导论
探索人工智能基础概念，研究数据驱动的网络模型、算法与平台。本课程梳理人工智能的关键发展节点，激发您的学习兴趣。通过此课程，您将牢牢掌握人工智能基础，为深度学习的最前沿铺路，不仅适合计算机专业学生，也对其他学科的学生适宜。
Artificial Neural Networks Theory and Its Applications
Embark on a journey through the intricate world of artificial neural networks, designed to introduce you to both their fundamental theories and cutting-edge applications. Initiate your learning with the basics of biological neural networks before delving into complex structures such as Deep Convolutional Neural Networks (CNNs) and Generative.
