Deep Learning courses

371 Courses

Coursera

Create Image Captioning Models - Français

Create Image Captioning Models - Français Dans ce cours, vous allez apprendre à créer un modèle de sous-titrage d'images à l'aide du deep learning. Vous découvrirez les différents composants de ce type de modèle, comme l'encodeur et le décodeur, et comment l'entraîner et l'évaluer. À la fin du cours, vous serez en mesure de créer vos propres mo.

Coursera

Create Image Captioning Models - 简体中文

Create Image Captioning Models - 简体中文本课程教您如何使用深度学习来创建图片标注模型。您将了解图片标注模型的不同组成部分，例如编码器和解码器，以及如何训练和评估模型。学完本课程，您将能够自行创建图片标注模型并用来生成图片说明。

University of Colorado Boulder

Introduction to Computer Vision

Introduction to Computer Vision Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will delve into modern deep learning methods, such as.

Coursera

MathWorks Computer Vision Engineer

MathWorks Computer Vision Engineer Prepare for a career in the rapidly expanding field of computer vision. The ability to extract meaningful information from visual data is crucial for efficiently developing smart monitoring systems, enhancing medical diagnostics, and powering the next generation of autonomous vehicles. This program is designed.

Google Cloud Skills Boost

Create Image Captioning Models

Create Image Captioning Models Discover how to create an image captioning model with deep learning in this comprehensive course. Learn about the vital components, including the encoder and decoder, and gain the skills to train and evaluate your model effectively. Upon completion, you will be proficient in developing your own image captioning mo.

Pluralsight

Implement Named Entity Recognition with BERT

In this course, you'll learn about the state-of-the-art transformer technique called BERT, how to tag respective news domain entities to classify information, and how to get relevant insights about geo-political news using a deep learning library. Classifying information into multiple domain entities is crucial for an enterprise to garner key i.

Coursera

Create Image Captioning Models - 한국어

Coursera

Create Image Captioning Models - 日本語版

Coursera

Create Image Captioning Models - בעברית

למדו ליצור מודלי הוספת כיתוב לתמונה - בעברית בקורס הזה תלמדו איך ליצור מודל הוספת כיתוב לתמונה באמצעות למידה עמוקה (Deep Learning). אתם תלמדו על הרכיבים השונים במודל הוספת כיתוב לתמונה, כמו המקודד והמפענח, ואיך לאמן את המודל ולהעריך את הביצועים שלו. בסוף הקורס תוכלו ליצור מודלים להוספת כיתוב לתמונה ולהשתמש בהם כדי ליצור כיתובים לתמונות. Universi.

Coursera

Create Image Captioning Models - Português Brasileiro

Crie Modelos de Legenda para Imagens - Português Brasileiro Neste curso, ensinamos a criar um modelo de legenda para imagens usando aprendizado profundo. Você vai aprender sobre os diferentes componentes de um modelo de legenda para imagens, como o codificador e decodificador, e de que forma treinar e avaliar seu modelo. Ao final deste curso,.

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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!