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Deep Learning courses

487 Courses

California Institute of Technology Udemy

Complete Python and Machine Learning in Financial Analysis

Udemy offers an extensive course titled "Complete Python and Machine Learning in Financial Analysis" that combines practical use of Python with advanced Machine Learning and Deep Learning methods tailored for financial analytics. This course is structured with step-by-step coding instructions alongside all required codes, enhancing your skills i.
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California Institute of Technology XuetangX

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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California Institute of Technology Chalmers University of Technology

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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California Institute of Technology XuetangX

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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California Institute of Technology XuetangX

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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California Institute of Technology XuetangX

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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California Institute of Technology XuetangX

现代图像分析

本课程面向职业、企业及社会，适合大学生训练，同样适合应用协作教师和社会行业企业相关人员的培训需求。课程涵盖从信息类大学生至中小学教师、高校教学人员以及所有希望提升图像处理能力的学员。 作为电子信息领域的重要专业课程，现代图像分析专注于数字图像处理技术及其基本应用。课程内容分为九个章节，三大部分。第一部分涵盖现代图像分析的基础知识，包括绪论、图像.
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California Institute of Technology XuetangX

智能车辆理论与应用

课程内容涵盖智能车辆的各个方面，如环境感知技术、深度学习及其在智能车辆上的应用、智能车辆SLAM、行为决策和运动规划等关键领域。特别介绍了智能网联技术以及智能车辆的测评体系。 本课程是国内较早开设的研究生课程，“智能车辆理论与技术”是北京理工大学机械工程学科的核心课程，通过丰富的案例理论联系实际进行讲解。
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California Institute of Technology XuetangX

现代光电图像处理方法

本课程是专业必修项目，提供32学时的校内讲授，面向学术和专业硕士研究生，每年约有60名学生参与。课程重点在于现代光电成像技术及其图像处理方法，采用专题模块的讲授形式。 每个专题全面覆盖从基本原理到理论方法，再到典型应用分析，旨在帮助学生融会贯通本科阶段所学课程，同时塑造工科思维。此外，课程紧跟技术发展前沿，重点反映当前光电图像处理方法的最新进展，拓.
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California Institute of Technology XuetangX

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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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!