|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-machine-learning-y/
课程评论：没有评论
课程名称：学习机器学习 课程概述：深度学习是人工智能（AI）中的一种方法，旨在培养计算机模仿人脑处理数据的方法。深度学习模型能够识别图片、文本、声音和其他数据中的复杂模式，以提供准确的洞见和预测。深度学习属于机器学习的广泛范畴，基于具有表示学习能力的人工神经网络。深度学习中“深”的含义是指网络中使用了多个层次。所采用的方法可以是监督学习、半监督学习或无监督学习。 深度学习架构，诸如深度神经网络、深度置信网络、深度强化学习、递归神经网络、卷积神经网络和变换器，已应用于计算机视觉、语音识别、自然语言处理、机器翻译、生物信息学、药物设计、医学图像分析、气候科学、材料检测和棋类程序等领域，并在许多情况下达到了与人类专家相当甚至超越的结果。 简单来说，深度学习是一类使用多个层次的机器学习算法，逐步从原始输入中提取更高级的特征。例如，在图像处理中，较低层可能识别边缘，而较高层可能识别人类所相关的概念，如数字、字母或面孔。从另一个角度来看，深度学习可以被视为一种“计算机模拟”或“自动化”人类学习过程，从源头（如狗的图像）到学习对象（狗）。因此，提出的“更深”学习或“最深”学习的概念显得有意义。 本课程将教您如何使用Rstudio构建和部署自己的深度学习模型。
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognise complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervisedDeep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performanceIn simple terms, Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.From another angle to view deep learning, deep learning refers to ‘computer-simulate' or ‘automate' human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Therefore, a notion coined as "deeper" learning or "deepest" learning [9] makes sense. The deepest learning refers to the fully automatic learning from a source to a final learned object. A deeper learning thus refers to a mixed learning process: a human learning process from a source to a learned semi-object, followed by a computer learning process from the human learned semi-object to a final learned object.In this course, you will learn how to build and deploy your own deep learning models using Rstudio
