Join

Deep Learning courses

487 Courses

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 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

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

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 Udemy

TensorFlow for Deep Learning Bootcamp

TensorFlow for Deep Learning Bootcamp | Udemy Enroll in the TensorFlow for Deep Learning Bootcamp offered by Udemy and elevate your skills in Artificial Intelligence, Machine Learning, and Deep Learning. This comprehensive course is designed to provide you with hands-on experience and in-depth knowledge using TensorFlow, Google's powerful open-so.
course image
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，.
course image
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.
course image
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.
course image
California Institute of Technology Udemy

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.
course image
California Institute of Technology Udemy

Deep Learning with TensorFlow (beginner to expert level)

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!