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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-artificial-neural-network/
课程评论：没有评论
**课程名称：深度学习基础** **课程概述：** “深度学习基础”课程旨在为对深度学习感兴趣的初学者介绍人工智能、机器学习以及深度学习的基本理论和Python实践。虽然具备大学水平的数学知识会更有帮助，但并非必需。 **核心内容：** * **人工智能、机器学习与深度学习的定义和关系：** 课程将解释人工智能如何使机器模仿人类智能，机器学习是人工智能的一个分支，而深度学习则是利用人工神经网络解决复杂问题的一种机器学习方法。 * **深度学习的优势：** 重点阐述深度学习如何克服传统机器学习在处理高维数据和手动特征提取方面的局限性，尤其是在图像和视频数据分析中的应用。 * **人工神经网络：** 介绍作为人工智能算法和深度学习模型核心的人工神经网络，以及它们如何模拟人脑神经元网络来解决复杂问题。 * **Python实践与Keras应用：** 课程包含Python编程教程，并使用Keras库来高效开发深度学习模型。基础Python知识会使学习更顺畅，但Python初学者也同样受欢迎。 **课程模块：** 1. **人工神经网络：** 涵盖人工神经网络的基本概念和原理。 2. **卷积神经网络 (CNN)：** 重点介绍适用于图像和视频数据处理的卷积神经网络。 3. **循环神经网络 (RNN)：** 讲解适用于时间序列分析和文本数据分析的循环神经网络。 **学习目标：** 完成本课程后，学员将对深度学习拥有扎实的基础知识。
Welcome to Deep Learning Fundamentals.This course covers the basic theory and Python practice of artificial neural networks. This course is designed for beginners who are interested in deep learning. Having knowledge of undergraduate level mathematics is preferable, but not a must.Artificial intelligence is a technology that makes machines imitate intelligent human behavior and human cognitive functions. Machine learning is a branch of artificial intelligence. It enables systems to learn from data automatically, that is, learn without being explicitly programmed. Deep Learning is a type of machine learning. It uses artificial neural networks to solve complex problems.One reason why deep learning has drawn much attention is that it overcomes the limitations of traditional machine learning. The first limitation is that traditional machine learning cannot handle high dimensional data. Thus, the performance of the traditional machine learning model tends to level off as the data amount increases. The second is that, when we use traditional machine learning techniques, we need to extract features manually. Therefore, when we analyze image data or movie data, traditional machine learning techniques are not suitable because such data contains a great number of features.Deep learning can overcome these limitations of traditional machine learning. An artificial neural network is one of the algorithms of artificial intelligence, and usually, it takes a form of a deep learning model. It simulates the network neurons that make up the human brain. The structure of an artificial neural network enables a deep learning model to solve complex problems that traditional machine learning algorithms can hardly handle.This course has some Python tutorials for developing deep learning models. And this course uses a library named Keras, which enables us to develop deep learning models efficiently. Basic-level Python knowledge is preferable, but Python beginners are also welcome.This course consists of three modules.1. Artificial Neural Networks2. Convolutional Neural Networks3. Recurrent Neural Networks.The first module is the basic of artificial neural network.The second module covers convolutional neural network that is a type of network effective for handling image and movie data.The third module covers recurrent neural network that is effective for time-series analysis and analyzing text data.After completing this course, you will have a fundamental knowledge of deep learning.I'm looking forward to seeing you in this course!
