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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-artificial-intelligence/
课程评论：没有评论
课程名称：初学者深度学习（人工智能）- 数据科学 课程概述：本课程旨在从零开始学习深度学习，深度学习是机器学习的延伸，专为想要掌握深度学习和人工智能基础的初学者设计。课程包含视频讲解，涵盖基本概念、详细理论及图形化解释。课程中解决了一些日常生活项目，均使用Python编程语言。所有章节附带可下载的电子书和Python代码文件，视频讲座生动有趣且简洁，帮助学员快速掌握内容。每个主题都深入讲解，力求以最简单易懂的方式涵盖所有相关领域，非常适合不懂机器学习基础的大学生。 课程的主要目标是以简单易懂的方式解释深度学习和人工智能。所有代码均通过Colab在线编辑器进行编写。Python作为一种广受欢迎的编程语言，因其简易上手的特点，已取代Java成为最广泛使用的入门编程语言，使初学者能够专注于理解编程概念，而非琐碎的细节。 课程覆盖的主要主题包括： - 深度学习简介 - 人工神经网络与生物神经网络 - 激活函数及其类型 - 人工神经网络（ANN）模型 - 复杂ANN模型 - 前向ANN模型 - 反向ANN模型 - ANN模型的Python项目 - 卷积神经网络（CNN）模型 - CNN模型中的滤波器或核 - 步幅技术与填充技术 - 池化技术与展平过程 - CNN模型的Python项目 - 循环神经网络（RNN）模型 - RNN模型的操作 - 一对一RNN模型 - 一对多RNN模型 - 多对多RNN模型 - 多对一RNN模型 这一课程非常适合初学者，旨在帮助学员打下坚实的深度学习基础，为进一步学习和发展打好基础。
Learn Deep Learning from scratch. It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don't know the fundamental of machine learning studying at college and university level.The main goal of publishing this course is to explain the deep learning and artificial intelligence in a very simple and easy way. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of different topics covered in Deep Learning:Introduction to Deep LearningArtificial Neural Network vs Biological Neural NetworkActivation FunctionsTypes of Activation functionsArtificial Neural Network (ANN) modelComplex ANN model Forward ANN modelBackward ANN modelPython project of ANN modelConvolutional Neural Network (CNN) modelFilters or Kernels in CNN modelStride TechniquePadding TechniquePooling TechniqueFlatten procedurePython project of a CNN modelRecurrent Neural Network (RNN) modelOperation of RNN modelOne-one RNN modelOne-many RNN modelMany-many RNN modelMany-one RNN model
