Neural Network Courses

305 Courses

人工智能

本课程的特色包括: 注重人工智能核心技术体系的构建,讲解全面完整,涵盖多个核心技术途径,如机器学习和神经网络等。 从“认识你自己”角度出发,将内容有机结合为一个整体,便于系统理解和掌握。 培养解决实际问题的思路,通过案例示例展示基本思路。 注重人工智能思维模式的建构,强调技术与非技术的区别。 注重技术创新思维的培养和建构。 本课程自2008年起.
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小白学人工智能

中国也在大力发展新一代人工智能技术,并致力于将其应用于各行各业。本课程完成后,学生将能够: 了解人工智能行业的最新应用和发展趋势。 从数据、算法和计算力的角度理解人工智能的发展。 用行业或生活术语比喻人工智能的概念和原理。 体验和理解深度学习原理,涉及CNN、图像风格迁移、RNN等架构。 通过实例理解深度学习特征,如输入层、隐藏层、输.
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人工智能技术

近年来在大数据、云计算、物联网等信息环境推动以及新的算法、模型和硬件助力下,人工智能在自然语言理解、语音识别、视觉分析和数据挖掘等领域取得了显著进展,成为社会经济发展的引擎。人工智能作为计算机科学的一个重要分支,是一门理论基础完善、多学科交叉且应用领域广阔的前沿学科,主要研究如何利用计算机模拟、延伸和扩展人类的智能行为。《人工智能技术》课程是计算.
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深度学习

本课程主要面向计科、人工智能及物联网专业的本科生,讲述深度学习基本概念、经典深度学习模型及其实践,主要内容包括前馈神经网络、深度模型优化与正则化、卷积神经网络、循环神经网络等,并介绍深度学习框架的编码实现和参数优化方法。本课程注重理论学习与实践应用的结合,除了课堂讲授之外,还将通过实践环节引导学生使用深度学习平台或工具,让学生通过实际应用来加深.
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基于图神经网络的事实验证

加入这场由XuetangX提供的中文讲座,探索如何利用图神经网络进行有效的事实验证。该课程专注于图形深度学习方法的应用,以自动化执行事实检查和验证的任务。参与者将有机会学习如何构建知识图谱、实现图神经网络架构,并通过分析结构化知识库中的实体与证据之间的关系来判定主张的准确性。 该课程特别适合对深度学习、神经网络和知识图谱领域感兴趣的学习者,希望深入.
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人工智能通识

2017年7月,中国国务院发布了《新一代人工智能发展规划》,将人工智能的发展提升为国家战略。这不仅有力支撑了高质量发展,还丰富了新生产力内涵,成为衡量国家科技创新和高端制造水平的关键指标。 西南交通大学计算机与人工智能学院推出的《人工智能》通识课程引领您探索推动第四次工业革命的人工智能技术。本课程自2017年开设以来,已成为全国高校中最早的人工智能教学.
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智能控制

了解智能和智能控制的基本概念,掌握智能控制发展历史及其在各领域的应用。课程内容涵盖专家系统、模糊控制、神经网络和进化计算的基础知识,介绍这些算法的仿真实现方法及其在实际工程中的应用。
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人工智能导论

人工智能导论 人工智能技术在各行业中扮演着日益重要的角色,通过推动经济和社会的高质量发展,极大地改变着人们的生活方式,包括衣食住行、工作学习以及娱乐休闲。智能设备、无人驾驶、语音助手、推荐系统等新兴产品为我们的日常生活增添了全新的体验。 人工智能的起源可追溯至计算机科学,为解决复杂问题而生。随着技术的演进,人工智能经历了符号主义到深度学习的多个发.
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人工智能

人工智能是计算机科学的一门前沿与交叉学科,本课程全面介绍人工智能的基础理论和基本技术。通过本课程的学习,要求学生能够了解人工智能的发展及其研究领域;掌握知识表示的各种方法,基本的问题求解技术以及基本的推理技术;并理解人工神经网络的基本结构和学习方法且能够初步理解专家系统、机器学习、自然语言处理等应用领域的知识。 提供者: XuetangX 类别: 人工智能课.
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Who is a neural network developer?

A neural network developer designs and programs hardware and software systems that operate on the principle of the human brain (neural networks).

Introduction to Neural Network Courses

A neural network developer is a programmer who creates software for mathematical models that work on the principle of the nervous system of a living organism.

A neural network is a computer programme built on the model of the structure and functioning of the human brain. Its constituent artificial neurons are tiny mathematical functions that perform computational actions - receive information, process and compare it, and pass it on. A neural network is not programmed in the usual sense of the word once and for all - it learns by loading and constantly processing huge data sets. For this purpose, special algorithms are used, which are created by the neural network developer. As a result, an artificial neural network can compare data, find patterns and on their basis make its own conclusions, classify information, predict events, recognise images, speech.

The task of a neural network developer is to create a programme capable of learning and teach it to learn. Examples of the results of neural network developers' work after neural network courseі include chatbots, voice assistants, text generators, mobile applications capable of recognising faces in photos or emotions in videos, navigation systems for unmanned cars, systems for detecting faults during maintenance, etc.

Career Paths and Learning Paths

Since, by and large, the creation of neural networks is one of the narrow specialisations of a Data Science specialist, the core knowledge of a neural network developer is Big Data science (data modelling, quality assessment of algorithms and prediction models). Also included in the knowledge pool are:

A lot of lectures on neural networks can be found on YouTube. Often after the video, machine learning enthusiasts do a detailed breakdown of the material. There are tutorial applications on the Internet (like artificial neural network course) with ready-made architectures that clearly demonstrate what is happening inside a neural network and give instructions on how to build it into a specific project.

Course Benefits and Features

Let's take a look at the main pluses of the neural networks and deep learning courseі:

These are just some of our advantages.

Enrollment Process

You can email us and sign up for our online lessons now. You can take a deep neural network course on one of the educational platforms. These courses are designed for people with no particular background, so they are suitable for most people. Online training is usually focused on practice - this allows you to quickly build up your portfolio and get a job immediately after training!