Deep learning machine learning

Artificial intelligence-machine learning

This article explains deep learning vs. machine learning and how they fit into the broader category of artificial intelligence. Learn about the deep learning solutions you can create in Azure Machine Learning, such as fraud detection, facial and speech recognition, sentiment analysis, and time series forecasting.

By using machine learning and deep learning techniques, you can compile machine systems and applications that perform tasks normally associated with human intelligence. These tasks include image recognition, speech recognition and language translation.

Now that you have some general information about machine learning and deep learning, let’s compare the two techniques. In machine learning, the algorithm must be told how to make an accurate prediction; to do so, it must gather more information (e.g., by performing feature extraction). In deep learning, on the other hand, the algorithm can obtain information on how to make an accurate prediction through its own data processing, thanks to the artificial neural network structure.

Neural networks and deep learning

Today, many terms related to artificial intelligence, machine learning and deep learning are widely used in the business context, especially when it comes to making correct predictions and analyzing data.

The production processes of today’s companies demand efficiency and automation. The market has grown, therefore, companies are increasingly focusing on the use of chatbots and other programs and systems to improve logistics, productivity and customer service, with a significant impact also on the presence and visibility of brands. In this regard, artificial intelligence, machine learning and deep learning are becoming increasingly important.

Some IT professionals determine that, using AI, machines can interpret a variety of data to achieve goals with greater flexibility, accuracy and efficiency.

Artificial intelligence is one of the most striking advances, as it enables machines to learn to predict certain types of behavior, based on data analysis. For this reason, it finds application in a variety of businesses, exploring machine learning capabilities on several fronts, such as:

Hacía el entendimiento del aprendizaje profundo y

El aprendizaje profundo (también conocido como aprendizaje estructurado profundo) forma parte de una familia más amplia de métodos de aprendizaje automático basados en redes neuronales artificiales con aprendizaje de representación. El aprendizaje puede ser supervisado, semisupervisado o no supervisado[1][2][3].

Las arquitecturas de aprendizaje profundo, como las redes neuronales profundas, las redes de creencias profundas, el aprendizaje de refuerzo profundo, las redes neuronales recurrentes y las redes neuronales convolucionales, se han aplicado a campos como la visión por ordenador, el reconocimiento del habla, el procesamiento del lenguaje natural, la traducción automática, la bioinformática, el diseño de fármacos, el análisis de imágenes médicas, la inspección de materiales y los programas de juegos de mesa, en los que han producido resultados comparables y, en algunos casos, superiores al rendimiento de los expertos humanos[4][5][6][7].

Las redes neuronales artificiales (RNA) se inspiran en el procesamiento de la información y los nodos de comunicación distribuidos en los sistemas biológicos. Las RNA tienen varias diferencias con los cerebros biológicos. En concreto, las redes neuronales artificiales tienden a ser estáticas y simbólicas, mientras que el cerebro biológico de la mayoría de los organismos vivos es dinámico (plástico) y analógico[8][9][10].

Some applications of deep learning (part-2c)

Deep learning is a type of machine learning that trains a computer to perform tasks like humans do, such as speech recognition, image identification, or making predictions. Instead of organizing data to run through predefined equations, deep learning sets basic parameters about the data and trains the computer to learn on its own by recognizing patterns through the use of many layers of processing.

Deep learning is one of the foundations of artificial intelligence (AI) and the current interest in deep learning is due in part to the rise of artificial intelligence. Deep learning techniques have improved the ability to classify, recognize, detect and describe – in a word, understand.

For example, deep learning is used to classify images, recognize speech, detect objects and describe content. Systems such as Siri and Cortana are powered, in part, by deep learning.