Deep Learning courses

636 Courses

FutureLearn

Artificial Intelligence for Healthcare

Delve into the transformative power of AI in the healthcare industry On this eight-week course, you’ll explore how AI is being used to turn the healthcare industry into a highly data-driven field. You’ll discover how different technologies are revolutionising areas in healthcare such as diagnostics, personalised medicine, and robotic surgery. With.

FutureLearn

Artificial Intelligence for Everyone

Discover essential AI techniques with Universiti Malaya On this eight-week course from Universiti Malaya, you’ll gain a solid understanding of artificial intelligence. Tailored specifically for beginners, you’ll be guided through essential AI concepts such as machine learning, natural language processing, and computer vision. By the end, you’ll ha.

Coursera

AI ML with Deep Learning and Supervised Models

This comprehensive AI ML with Deep Learning and Supervised Models specialization equips you with the skills to excel in roles across AI, machine learning, and deep learning. Through in-depth modules, you'll master regression, classification, clustering, neural networks, and advanced AI frameworks to solve real-world challenges. By the end of this c.

Treehouse

(UPI) Chapter 19: AI in Cybersecurity Course (How To)

Chapter 19 explores how AI, particularly deep learning models like RNNs and CNNs, enhances malware detection by analyzing static and dynamic features, addressing the growing complexity of cyber threats with automated precision.

edX

Generative AI Engineering

The gen AI market is projected to grow at an impressive 46% CAGR through 2030 (Statista). The demand for tech professionals skilled in generative AI engineering is skyrocketing! This Generative AI Engineering Professional Certificate gives aspiring generative AI engineers, AI developers, data scientists, machine learning engineers, and AI research.

edX

IBM Applied AI Developer

92% of companies plan to increase their AI investments over the next three years (McKinsey). Talented AI developers are in high demand! This IBM Applied AI Developer Professional Certificate gives aspiring AI developers the job-ready skills to build AI-powered applications, virtual assistants, and chatbots employers are looking for in just 6 month.

edX

Foundations of AI

71% of executives say they prefer to hire a candidate with AI skills (Forbes); the need to get up to speed with AI has never been more critical. This IBM Professional Certificate gives you a practical understanding of the core concepts and real-world application of AI, plus job-ready skills using gen AI in just 9 weeks. Whether you’re an experienc.

edX

Generative AI for Everyone

97% of employers expect to benefit from using generative AI (Source: Access Partnership survey for Amazon Web Services). Organizations need individuals who know how to automate tasks, speed up research, analyze data swiftly, and create original content using gen AI. This generative AI Professional Certificate is ideal for anyone looking to build i.

Harvard University

Tiny Machine Learning (TinyML)

In this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology. TinyML is a cutting-edge field that brings the transformative power of machine.

Johns Hopkins University

Practical Methodology and Ethics in AI

Discover the comprehensive course "Practical Methodologies and Ethics in AI" offered by Johns Hopkins University on Coursera. This program is designed to furnish you with vital skills to develop, evaluate, and deploy deep learning models, while thoroughly exploring ethical considerations in AI. Engage in hands-on projects and case studies to.

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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:

Face recognition by the DeepFace system is about 97%, and this result is about the same as that of the average person.

Imitation of human speech by the WaveNet system. This is not reading the text, but composing sentences with accents, without errors in stress and without unnatural pauses.

Making medical diagnoses. For example, determining diabetic retinopathy from photographs of the eyes. The technology is already being used in US medical institutions.

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:

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.

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.

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.

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!