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所在平台: Udemy |
课程主页: https://www.udemy.com/course/3-real-world-deep-learning-projects/
课程评论：没有评论
课程名称：三个真实世界的深度学习项目 课程概述：本课程旨在深入探讨深度学习，您将通过实际项目学习不同的概念。这些项目将助您在大学项目和面试中获得竞争优势。每节讲座后还将提供练习题，以帮助您更好地理解概念。 深度学习是一种机器学习的子集，本质上是具有三个或更多层的神经网络。它试图模拟人脑的行为，尽管远未达到其能力，深度学习允许模型从大量数据中“学习”。深度学习技术驱动着许多人工智能应用和服务，改善自动化，能够在没有人类干预的情况下执行分析和物理任务。这项技术在日常产品和服务（如数字助手、语音启用的电视遥控器和信用卡欺诈检测）以及新兴技术（如无人驾驶汽车）背后发挥着重要作用。 深度学习不仅涉及尖端复杂的应用，它现已成为机器学习、数据科学和统计学的标准工具。此外，深度学习被小型初创公司用于数据挖掘和降维，政府用于检测逃税，科学家则用于研究数据中的模式识别。深度学习在技术、商业和娱乐各个领域中得到广泛应用，并且每年变得愈加重要。 深度学习的工作原理基于一个简单的原则：采用一个超级简单的算法（加权和及非线性），并多次重复，直到结果成为数据的一个极其复杂和精细的学习表示。虽然这个想法很简单，但深度学习的核心理念实际上略显复杂。由于深度学习模型设计、参数和应用的多样性，真正掌握深度学习需要有经验的老师指导您学习数学、实现以及推理，并提供大量的实践示例和练习题。 总之，深度学习主要是应用数学，而数学并不是一项观赛运动，您需要主动参与以真正理解这门技术。
What is this course all about?Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will learn about different concepts through doing real life projects.These projects will help you to get an edge in college projects and interviews.We will also add practice questions after each lecture so that you can have good understanding about the concepts.What is deep learning?Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain-albeit far from matching its ability-allowing it to "learn" from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).Deep learning is increasingly dominating technology and has major implications for society.From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.How does deep learning work?Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;) but that's the core idea, and everything else - literally everything else in deep learning - is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning - I mean, really learn deep learning, not just have superficial knowledge from a youtube video - by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!
