|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-from-scratch-in-python/
课程评论：没有评论
**课程名称：** AI & Deep Learning From Scratch In Python (Python 从零开始的AI与深度学习) **课程概述：** 本课程旨在帮助学员深入理解卷积神经网络（CNN）的工作原理。课程将对CNN的每一个组成部分进行数学上的讲解，随后在Python中进行实现。通过交互式编程练习，学员可以逐步构建一个基于优化卷积神经网络模型的完整物体检测框架。 课程无需任何先验知识。专门为Python编程基础和深度学习所需微积分知识设置的章节，将提供学习课程和实现卷积神经网络的必要基础。 在本课程中，学员将接触到目前最新的、也是最成功的实时多目标检测算法。学员将全面理解反向传播过程，不仅从数学理论角度，也从编程实现角度，从而为神经网络训练这一核心环节打下坚实基础。 课程结束后，学员将具备实现一个先进的卷积神经网络框架的实操经验。该框架将集成最新的优化和正则化技术，使学员能够有效地应对复杂的现实世界物体检测任务，并取得优异的性能表现。这些实践知识将助力学员在令人兴奋的计算机视觉和深度学习领域拓展技能。
This course is for anyone willing to really understand how Convolutional Neural Networks (CNNs) work. Every component of CNNs is first presented and explained mathematically, and the implemented in Python.Interactive programming exercises, executable within the course webpage, allow to gradually build a complete Object-Detection Framework based on an optimized Convolutional Neural Network model. No prior knowledge is required: the dedicated sections about Python Programming Basics and Calculus for Deep Learning provide the necessary knowledge to follow the course and implement Convolutional Neural Networks.In this course, students will be introduced to one of the latest and most successful algorithms for real-time multiple object detection. Throughout the course, they will gain a comprehensive understanding of the Backpropagation process, both from a mathematical and programming perspective, allowing them to build a strong foundation in this essential aspect of neural network training.By the course's conclusion, students will have hands-on experience implementing a sophisticated convolutional neural network framework. This framework will incorporate cutting-edge optimization and regularization techniques, enabling them to tackle complex real-world object detection tasks effectively and achieve impressive performance results. This practical knowledge will empower students to advance their capabilities in the exciting field of Computer Vision and Deep Learning.
