How to design deep learning and what is it

Deep learning (DL) is a new research direction in the field of machine learning (ML), which was introduced into machine learning to bring it closer to the original goal of artificial intelligence (AI).

Deep learning is the study of the intrinsic rules and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. The ultimate goal is for machines to be able to learn analytically like humans, and to recognize data such as text, images, and sounds.

Deep learning is a sophisticated machine learning algorithm that achieves far more performance in speech and image recognition than previous technologies. Deep learning has achieved a lot in search technology, data mining, machine learning, machine translation, natural language processing, multi** learning, speech, recommendation and personalization techniques, and other related fields.

Deep learning enables machines to imitate human activities such as audio-visual and thinking, solves many complex pattern recognition problems, and makes great progress in artificial intelligence-related technologies.

Deep learning AI scans heart blood flow

In a medical and artificial intelligence (AI) study published in the British journal Nature Machine Intelligence on the 13th, Swiss scientists introduced an artificial intelligence system that can scan cardiovascular blood flow in seconds. This deep learning model has the potential to optimize diagnostic workflows by allowing clinicians to observe changes in blood flow in real time while patients undergo MRI scans.

Four-dimensional (4D) MRI scans can be used to reconstruct the characteristics of cardiovascular blood flow over time, which is of great significance for the diagnosis of cardiovascular diseases. However, these scans typically require a processing time of 20 minutes, which means that imaging cannot be further evaluated during the scan. Speeding up these scans allows the patient to be assessed at the same time as the scan, saving not only the clinician's time, but also the patient's discomfort.

This time, Valéry Weschenevski, a researcher at ETH Zurich in Switzerland, and his colleagues have developed a deep learning artificial intelligence model that can reconstruct blood flow through the heart in four dimensions in seconds. The research team trained a neural network with 11 scan cases and found that the network could accurately reconstruct aortic blood flow in both normal and abnormal patients with the same accuracy as conventional methods.

Currently, the AI system can also reconstruct a scan in about 20 seconds, which is 30 times faster than today's cutting-edge traditional methods and twice as fast as previous deep learning methods.

Deep learning is a method of machine learning based on the representation of data. Deep learning is a new field in machine learning research, motivated by the creation and simulation of neural networks that mimic the human brain for analytical learning, which mimics the mechanisms of the human brain to interpret data, such as images, sounds, and text. Like machine learning methods, deep machine learning methods are divided into supervised learning and unsupervised learning, and the learning models built under different learning frameworks are very different.

For example, convolutional neural networks are a machine learning model based on deep supervised learning, while deep belief networks are a machine learning model based on unsupervised learning. The benefit of deep learning is that it replaces manual feature acquisition with unsupervised or semi-supervised feature learning and hierarchical feature extraction efficient algorithms.

Deep learning is a meaningful learning process in which students actively participate, experience success, and develop around challenging learning topics under the guidance of teachers. In this learning process, students master the core knowledge of the discipline, understand the learning process, grasp the essence of the discipline and the ideological methods, form positive memory learning motivation, advanced social emotions, positive attitudes, and correct values, and become excellent learners with independence, criticism, creativity, and cooperative spirit, and a solid foundation, and become the masters of future social and historical practice.

The concept of deep learning originated from the study of artificial neural networks. A multilayer perceptron with multiple hidden layers is an example of a deep learning structure. Deep learning discovers distributed feature representations of data by combining low-level features to form more abstract high-level representation attribute categories or features.

The "depth" of deep learning is an attribute word, and deep learning is a very deep degree of learning. The antonym of deep learning is very shallow learning, that is, superficial learning, superficial learning, shallow learning, etc.

Deep learning is to study in detail on the basis of the original knowledge, deepen understanding, and strive to find breakthroughs, which is deep learning.

Deep learning means that you can concentrate on learning, and you can really understand this knowledge and understand it thoroughly.

We'll look at how deep learning works and move on to how it differs from machine learning and artificial intelligence. We'll also take a look at what neural networks are and how they are trained to recognize handwritten numbers.