Join

Deep Learning courses

635 Courses

California Institute of Technology XuetangX

万物互联

庄子曾言：“圣人者，原天地之美而达万物之理”。他通过对天地壮美的感受洞悉了宇宙万物的演化规律。随着科技的发展，感知设备能够更加智能地连接人与信息，把现实与数字世界无缝衔接，万物互联的新时代即将开启。我们的课程将引领你进入这个高能的万物互联时代。 课程沿时间线梳理万物互联的发展历程，深入讲解互联网的不同发展阶段，细致剖析万物互联的事物、数据、人员和流程.
course image
California Institute of Technology XuetangX

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.
course image
California Institute of Technology XuetangX

融合知识的预训练语言模型

course image
California Institute of Technology XuetangX

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.
course image
California Institute of Technology XuetangX

走进人工智能（人工智能概论）

走进人工智能（人工智能概论）课程是一门专为高职专科生设计的课程，旨在帮助学习者了解并感知人工智能技术在日常生活中的应用。课程设计理念通过将复杂的技术理论与实际应用场景结合，以便学习者通过生活感知人工智能，并通过视频、图文、讨论等多元化形式激发兴趣。学习者还可以通过作业和考试环节加深理解和巩固学习成果。 课程涵盖七大模块，包括：人工智能的发展历史、.
course image
California Institute of Technology XuetangX

机器学习入门

欢迎加入系统、科学且专业的机器学习入门课程。这门课程不仅涵盖传统的机器学习内容，还包括了深度学习的先进知识。您将学习到机器学习和深度学习的基础理论、算法原理及数据处理技巧，并且通过实际案例分析提高自己的实践能力，从而为未来的学习和职业发展奠定坚实基础。 这是一门针对机器学习初学者设计的课程，涵盖广泛的基本知识，适合各专业背景的学生参与学习。无论您的.
course image
California Institute of Technology XuetangX

论文一作学者分享大模型前沿研究

论文一作学者分享大模型前沿研究 Discover the forefront of AI advancement by learning about the latest developments in large language models from a lead research author. This Chinese-language academic presentation offers a unique opportunity to acquire deep technical understanding of current research directions, innovative methodological approac.
course image
California Institute of Technology XuetangX

人工智能（通识课）

人工智能在互联网时代获得了前所未有的发展机遇，已经成为目前发展最迅速、对社会影响最大的新兴学科。由于人工智能是模拟人类智能解决问题的方法，几乎在所有领域都具有非常广泛的应用，所以，目前许多高校开设了大学生人工智能通识课程。 课程涵盖了人工智能的概念、发展简史、研究内容及应用，引导学生深入各个研究领域，并介绍知识表示、推理方法、搜索策略、遗传算.
course image
California Institute of Technology XuetangX

AI TIME：深度推荐系统的探索与实践

AI TIME: 深度推荐系统的探索与实践 Delve into the world of deep recommendation systems with our in-depth lecture conducted in Chinese, brought to you by XuetangX. This course offers a comprehensive exploration of both the theoretical underpinnings and practical implementations of recommendation algorithms. Engage with key concepts and examine how.
course image
California Institute of Technology XuetangX

初探人工智能

course image

Artificial intelligence is moving towards becoming on the same level as the living human mind. In such dangerous proximity to the execution of one of the futurological scenarios, it becomes a little scary, but at the same time very interesting. Artificial intelligence is nurtured by machine learning specialists. In the last decade, the deep learning method has been developing, and its results are already impressive.

What is deep learning?

“Deep learning” – literally “deep learning”. This is about artificial intelligence and increasing its abilities through training, based not on artificial codes, but on principles similar to the development of human intelligence. Deep learning methods make it possible to make machines self-learning.

The term itself and developments in this area appeared 40 years ago, but until 2012 they could not be applied in practice, as they were limited by insufficient technical capacity. Now there are already published works by the pioneers of deep learning, and textbooks and training courses in this specialty are gradually appearing.

Deep learning on your fingers: The ability of a machine to find an answer using calculations is called artificial intelligence. A machine can be taught to learn independently by building appropriate algorithms - this is called machine learning. With this approach, coded algorithms will no longer be needed to solve problems. The process of acquiring and using skills imitates human thinking and is called deep learning.

What tasks can be performed using deep learning right now?

If at the dawn of automation machines learned to do mechanical work for humans, now machines are learning to do routine intellectual work for us. The further progress we make, the more tasks we can shift to them, freeing up time for what really matters.

Officially, the main task of deep learning is the automation of complex tasks in various areas of human activity. It's like a computer, only of a different century and a different level.

But of particular interest is the neural network’s assistance in creating programs for solving cognitive problems.

Enough general phrases, let's move on to examples:

It’s hard to even imagine what awaits us in the future if people outside of IT have just heard about deep machine learning, and it has already produced such amazing results.

Why study deep learning?

To earn twice as much as ordinary IT specialists. Progress in the field of information technology is not just walking, but actually running, and it’s time to benefit from it. The sphere is not yet oversaturated, and oversaturation will not happen soon. Still, creating neural networks is not as simple as filing nails or maintaining Instagram accounts. But now is the time to start studying in order to develop along with your specialty and, perhaps, soon become someone who develops it.

Deep learning courses that currently exist are divided into four categories. Decide for yourself which one is for you:

  1. Trainings are highly specialized classes for practicing specific skills. Suitable for those who need to form an understanding of the basic principles of machine thinking.

  2. Long courses - for AI specialists and those involved in database analysis. Long-term deeplearning ai courses are not for everyone and require patience and time.

  3. University programs - for maximum immersion in the subject. They may be too difficult for beginners, although the application of effort will give results that should not be expected from short courses.

  4. A short best deep learning course on technology in business - general information for managers who will not be doing it themselves, but need to have an understanding of the subject.

You will have to put in a lot of effort, but the result is worth it. Just for fun, you can look at vacancies for deep learning specialists on sites with job offers and evaluate upcoming prospects. Not everyone needs deep learning experience yet, and soon all the sweet jobs will require several years of practice. So, if you have the ability to train soulless machines that are almost equal to us in intelligence, hurry up to take up vacant positions after a deep learning online course from AI Eeducation!