Join

Deep Learning courses

487 Courses

California Institute of Technology Coursera

Deep Learning

Deep Learning Course | Coursera Join our comprehensive Deep Learning course on Coursera and dive into the fascinating world of advanced machine learning. This course is meticulously designed to cover: Neural Networks Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Transformers Generative Models Neur.
course image
California Institute of Technology AWS Skill Builder

Fundamentos de machine learning e inteligência artificial (Português) | Fundamentals of Machine Learning and Artificial Intelligence (Portuguese)

Fundamentos de Machine Learning e Inteligência Artificial (Português) | Fundamentals of Machine Learning and Artificial Intelligence (Portuguese) Neste curso, você aprenderá sobre os fundamentos de machine learning (ML) e inteligência artificial (IA). Você explorará as conexões entre IA, ML, aprendizado profundo e o campo emergente da inteligência.
course image
California Institute of Technology Udemy

Derin Öğrenme ile Görüntü İşleme: Python OpenCV Keras (Gİ-2)

course image
California Institute of Technology University of Colorado Boulder

컴퓨터 비전 분야에서의 딥 러닝 응용 사례

컴퓨터 비전 분야에서의 딥 러닝 응용 사례 이 강의는 CU 볼더 대학교의 데이터 과학 석사(MS-DS) 학위 과정의 일부로써 학점 인정이 가능하며 Coursera 플랫폼을 통해 제공됩니다. MS-DS는 CU 볼더 대학교의 응용 수학, 컴퓨터 과학, 정보 과학 및 기타 여러 학과 교수진이 모여 만든 학제간 학위 과정입니다. MS-DS는 능력에 따라 입학이 허가되고 지원 절차가 없기 때문에.
course image
California Institute of Technology Coursera

Create Image Captioning Models - 繁體中文

Create Image Captioning Models - 繁體中文 本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分，例如編碼器和解碼器，以及如何訓練和評估模型。在本課程結束時，您將能建立自己的圖像說明生成模型，並使用模型產生圖像說明文字。 提供者： Coursera 分類： 深度學習課程, 圖像處理課程, 模型評估課程, 圖像說明生成課程,.
course image
California Institute of Technology Coursera

Create Image Captioning Models - Español

Create Image Captioning Models - Español Create Image Captioning Models - Español En este curso, se te enseña a crear un modelo de generación de leyendas de imágenes con el aprendizaje profundo. Aprenderás sobre los distintos componentes de los modelos de generación de leyendas de imágenes, como el codificador y el decodificador, y cómo entrena.
course image
California Institute of Technology 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.
course image
California Institute of Technology 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。
course image
California Institute of Technology 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.
course image
California Institute of Technology 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.
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!