Deep Learning courses

371 Courses

Coursera

Intel® Technical Pro – Principles of AI Software & Ecosystem

Intel® Technical Pro – Principles of AI Software & Ecosystem In the era of AI everywhere, businesses are reimagining every aspect of their operations, from finance to compliance, to see how AI can augment and automate workflows. Intel is helping businesses think differently about their enterprise AI strategies from the client to the edge to the.

AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Simplified Chinese)

AWS ML Engineer Associate Curriculum Overview (Simplified Chinese) 在这个 AWS ML Engineer Associate Curriculum 的入门课程中，您将回顾机器学习 (ML) 基础知识并研究 ML 和 AI 的演变。您将探索 ML 生命周期的初始步骤，确定业务目标并根据该业务目标制定 ML 问题。最后，您将了解 Amazon SageMaker，这是一项完全托管式 AWS 服务，可用于.

PyTorch for Deep Learning

PyTorch for Deep Learning Learn PyTorch and become a proficient Deep Learning Engineer. This PyTorch course is a step-by-step guide designed to help you develop your own deep learning models. The curriculum includes essential topics such as Computer Vision, Neural Networks, and much more.

Coursera

GenAI for Data Scientists

GenAI for Data Scientists GenAI for Data Scientists is tailored for professionals eager to integrate Generative AI (GenAI) into their data science practices. This introductory course simplifies the complex realm of GenAI, illustrating its remarkable impact on data analysis, predictive modeling, and more. You will gain a thorough understanding o.

AWS Skill Builder

AWS SimuLearn: TensorFlow and Computer Vision

AWS SimuLearn: TensorFlow and Computer Vision AWS SimuLearn is an online learning experience that pairs generative AI-powered simulations with hands-on practice to help individuals learn how to translate business problems into technical solutions through the simulation of dialog between a customer and a technology professional. AWS SimuLearn: Te.

LinkedIn Learning

Generative AI: Introduction to Large Language Models

Generative AI: Introduction to Large Language Models | LinkedIn Learning Course Title: Generative AI: Introduction to Large Language Models Description: Gain a foundational knowledge of how large language models and other Generative AI models work. University: Provided by LinkedIn Learning Categories: Artificial Intelligence Cour.

LinkedIn Learning

Introduction to Generative Adversarial Networks (GANs)

Introduction to Generative Adversarial Networks (GANs) Gain a better understanding of Generative Adversarial Networks (GANs). Learn how GANs are created, trained, and their capability to generate new media. This course is offered by LinkedIn Learning through the university platform. Categories: Arti.

LinkedIn Learning

AI Fundamentals for Data Professionals

AI Fundamentals for Data Professionals | LinkedIn Learning Discover the fundamental skills, tools, and concepts of AI in this course designed for data professionals. Gain expertise in the core areas of Artificial Intelligence, including Machine Learning, Reinforcement Learning, Deep Learning, Supervised Learning, Unsupervised Learni.

Coursera

AI-Powered Software and System Design

AI-Powered Software and System Design | Coursera AI-Powered Software and System Design - Coming soon! Provider: Coursera Categories: Machine Learning Courses, Deep Learning Courses

AWS Skill Builder

Introduction to AWS Inferentia and Amazon EC2 Inf1 Instances (Japanese)

Introduction to AWS Inferentia and Amazon EC2 Inf1 Instances (Japanese) この動画では、機械学習の推論処理の課題とユースケースについて学び、これらの課題の解決に役立つ、AWS Inferentia 搭載の Amazon EC2 Inf1 インスタンスを使った AWS ソリューションについて理解します。機械学習の推論処理用に設計された AWS Inferentia のカスタムチップや、AWS Inferentia を.

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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!