Join

Deep Learning courses

487 Courses

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.
course image
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.
course image
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.
course image
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.
course image
California Institute of Technology XuetangX

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.
course image
California Institute of Technology XuetangX

人工智能导论

探索人工智能基础概念，研究数据驱动的网络模型、算法与平台。本课程梳理人工智能的关键发展节点，激发您的学习兴趣。通过此课程，您将牢牢掌握人工智能基础，为深度学习的最前沿铺路，不仅适合计算机专业学生，也对其他学科的学生适宜。
course image
California Institute of Technology XuetangX

Artificial Neural Networks Theory and Its Applications

Embark on a journey through the intricate world of artificial neural networks, designed to introduce you to both their fundamental theories and cutting-edge applications. Initiate your learning with the basics of biological neural networks before delving into complex structures such as Deep Convolutional Neural Networks (CNNs) and Generative.
course image
California Institute of Technology XuetangX

Introduction to Artificial Intelligence（人工智能导论）

Explore the fundamentals of artificial intelligence with our comprehensive course, "Introduction to Artificial Intelligence（人工智能导论）." This course, delivered by XuetangX, covers five insightful chapters designed for learners keen on understanding AI's core concepts and applications. Chapter 1: Introduction to Artificial Intelligence In.
course image
California Institute of Technology XuetangX

Cloud Computing and Artificial Intelligence

This course covers the structure and possibilities of Cloud Computing, emphasizing the merging of data with AI services like IoT, key technologies in the Fourth Industrial Revolution. It also delves into the applications of big data processing through text analytics. Participants will gain an understanding and practical skills in the principles o.
course image
California Institute of Technology Udemy

All of AI: ChatGPT, Midjourney, Stable Diffusion & App Dev

Find this AI course on Udemy Explore various aspects of artificial intelligence, from ChatGPT and prompt engineering to OpenAI API and beyond. This course covers diverse topics such as AI app development, chatbots, GPTs, and machine learning. Get hands-on experience with generative AI, automation, and copilot functionalities. Perfect for anyon.
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!