Deep Learning courses

487 Courses

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,.

Udemy

TensorFlow for Deep Learning Bootcamp

TensorFlow for Deep Learning Bootcamp Learn TensorFlow by Google and become an expert in AI, Machine Learning, and Deep Learning! Provider: Udemy Categories: Machine Learning Courses, Computer Vision Courses, Deep Learning Courses, TensorFlow Courses

Pluralsight

Demystifying Image Recognition: Dive into Deep Learning

Demystifying Image Recognition: Dive into Deep Learning In today's data-driven world, a significant amount of data is visual. This course, Demystifying Image Recognition: Dive into Deep Learning, will equip you with the skills to recognize and classify images using neural networks. Throughout the course, you'll gain comprehensive knowledge and.

Pluralsight

Image Segmentation

Image Segmentation Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation. You want your application to consume digital images and convert them to.

Pluralsight

Using Neural Networks for Image and Voice Data Analysis

Using Neural Networks for Image and Voice Data Analysis Neural networks can be configured in various ways depending on the type of data and objectives. This course will help you understand how to properly choose a neural network architecture for image or audio data. Deep learning, as opposed to machine learning, allows a more robust way to dea.

1 ... 29 30 31 32 33 ... 49

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!