Deep Learning courses

487 Courses

Curso Completo de Deep Learning

Este proyecto es un curso práctico y efectivo para aprender Deep Learning con ejercicios aplicados. Aprenderás desde cero los fundamentos del Deep Learning. Después irás aprendiendo a desarrollar redes neuronales con Python y Keras a través de ejercicios prácticos. Gracias a este curso aprenderás a programar tus propios modelos de Deep Learni.

Diabetic Retinopathy Detection with Artificial Intelligence

Diabetic Retinopathy Detection with Artificial Intelligence In this project, we will train a deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the deve.

Coursera

Fake Instagram Profile Detector

Fake Instagram Profile Detector In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major issue in social media, often creating an illusion of numerous followers for influencers or being used to impersonate others and sell fake services or p.

Coursera

Paid Course

1-2 hours

On-Demand

Coursera

Create Machine Learning Models in Microsoft Azure

Title: Create Machine Learning Models in Microsoft Azure Description: Machine learning forms the foundation for predictive modeling and artificial intelligence. In this course, you will learn both the underlying concepts and practical skills needed to build models using the most common machine learning tools. The curriculum covers core princip.

Coursera

Free Online Course (Audit)

13 hours

On-Demand

Coursera

Transformer Models and BERT Model - Português Brasileiro

Transformer Models and BERT Model - Português Brasileiro Este curso é uma introdução à arquitetura de transformador e ao modelo de Bidirectional Encoder Representations from Transformers (BERT, na sigla em inglês). Você vai aprender sobre os principais componentes da arquitetura de transformador, como o mecanismo de autoatenção, e como eles sã.

Coursera

Free Online Course (Audit)

On-Demand

Coursera

Transformer Models and BERT Model - Italiano

Transformer Models and BERT Model - Italiano Questo corso ti introduce all'architettura Transformer e al modello BERT (Bidirectional Encoder Representations from Transformers). Scopri i componenti principali dell'architettura Transformer, come il meccanismo di auto-attenzione, e come viene utilizzata per creare il modello BERT. Imparerai anche l.

Coursera

Free Online Course (Audit)

On-Demand

Vanderbilt University

Natural Language Processing and Capstone Assignment

Natural Language Processing and Capstone Assignment | Vanderbilt University | Coursera Welcome to Natural Language Processing and Capstone Assignment. This course dives into how technical and business techniques can be harnessed to deliver business insights, competitive intelligence, and consumer sentiment. Hosted by Vanderbilt University on Cou.

Coursera

Free Online Course (Audit)

4-5 hours

On-Demand

Udacity

Deep Learning Topics with Computer Vision and NLP

Deep Learning Topics with Computer Vision and NLP In this course, you will learn how to train, finetune, and deploy deep learning models using Amazon SageMaker. You'll begin by understanding what deep learning is, its applications, and the tools used by deep learning engineers. Next, we will explore artificial neurons and neural networks and the.

Udacity

Paid Course

4 weeks, 3-4 hours a week

On-Demand

Udacity

Introduction to Neural Networks with TensorFlow

Introduction to Neural Networks with TensorFlow | Udacity Master the basics of neural networks using Python and TensorFlow, and apply your knowledge to build a functional image classifier. This course guides you through training a deep learning model on an image dataset and deploying it to classify new images. University: Udacity Categories: Pyt.

Udacity

Paid Course

3 weeks, 5-6 hours a week

On-Demand

Udacity

RNNs and Transformers

RNNs and Transformers - Udacity Explore the intricacies of RNN architectures and their design patterns in this comprehensive course offered by Udacity. Additionally, gain a deep understanding of transformer architectures and how they differ from traditional RNN models. Perfect for those interested in deep learning, sentiment analysis, and advanc.

Udacity

Paid Course

4 weeks

On-Demand

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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!