Neural Network Courses

305 Courses

Complete A.I. & Machine Learning, Data Science Bootcamp

Dive into the world of A.I. and machine learning through this thorough Data Science Bootcamp. Master the essentials of data analysis and machine learning using Python, TensorFlow, and Pandas. Join our course, provided by Udemy, and leverage expert insights into deep learning and data visualization. This bootcamp caters to a wide range of inte.
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计算机是如何实现智能的

Join a captivating Chinese language lecture by Tsinghua University, delving into how computers achieve intelligence. This course uncovers the foundational concepts of artificial intelligence and computer intelligence, highlighting the processes of information processing, decision making, and the simulation of human cognitive functions. Learn.
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人工智能与医学数据计算

《人工智能与医学数据计算》课程共分为十节课。课程从人工智能与医学数据计算的背景知识入手,提供人工智能和深度学习的发展概述。第二课分析人工智能目标,介绍关键技术及相关概念,重点解析两种不同人工智能技术的区别与联系。 第三和第四节课概述人工智能的基本应用场景及操作环境的软硬件要求,为后续的深度学习关键技术学习奠定基础。第五节课关注深度学习网络架构,.
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AI TIME PhD ICLR专场六

Delve into the forefront of artificial intelligence and machine learning research during the AI TIME PhD ICLR专场六 session. This expertly curated event brings together leading minds in academia and industry to explore and discuss cutting-edge topics, all delivered in Mandarin. Attendees will gain valuable insights into recent advancements in ne.
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脑科学与认知

大脑作为人类思维的中枢,是接受外界信号、形成意识和发出指令的核心机构。尽管脑科学已取得显著进展,仍有许多未解的谜团。作为21世纪的前沿科学之一,脑科学为我们理解自然和自身提供了深刻的视角。 《脑科学与认知》课程为生物医学工程、智能医学工程及人工智能专业的学生提供了探索脑与认知科学的理论和应用平台。本课程从基础到应用,全面概述脑科学和认知科学的基本.
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人工智能基础

本课程全面介绍了人工智能的基础原理,涵盖四大方面:搜索与问题求解、知识与推理、学习与发现以及具体应用领域。搜索与问题求解涉及问题解决的基本原理、策略与图搜索以及博弈;知识与推理涵盖谓词逻辑、归结原理与确定性推理;学习与发现部分涉及机器学习知识,包括分类、回归和聚类算法;深度学习入门涉及图像识别、卷积神经网络、自然语言处理及循环神经网络。 通过本课程.
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人工智能基础

本课程专为电气信息类专业本科生设计,重点介绍人工智能的前沿技术及其应用基础。课程内容覆盖广泛,通过系统的知识讲解和丰富的研究案例,学生将拓宽视野,增进科学素养,并优化知识结构。结合最新的人工智能技术讨论,配以视频资料和文献,激发学生对各研究领域的兴趣,提高学习热情,掌握创新方法,培养将理论与实际相结合的能力,以及问题解决能力。与类似课程相比,本课程.
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大数据与机器智能

在这门课程中,您将掌握Python编程技能并进行机器智能的实验。通过深入学习机器学习的基本原理,您将完成机器智能的课程项目。参与Scikit-learn、TensorFlow2和Keras2技术的实践,体验计算机视觉和语音识别等人工智能应用。 此外,课程帮助您理解人工智能产业的发展方向和生态系统。您将熟悉各种技术工具及应用,拓展人工智能领域的视野,培养利用人工智能技术进行创新和.
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神经网络理论及应用

Course Objective Since the mid-1980s, artificial neural networks have rapidly evolved into a forefront research field in information technology. This parallel information processing system significantly impacts fields such as computer science, AI, cognitive science, neuroscience, information science, automatic control, and robotics. The course "N.
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大模型技术及交叉应用

Embark on a comprehensive journey to understand the intricacies of large-scale AI models and their diverse applications. With XuetangX, delve into nine meticulously structured lessons that encompass the essential principles, intricate technical implementations, and practical applications across various sectors. Whether you're passionate about.
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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!