Deep learning is critical to improve things like speech recognition, natural language processing, and even computer vision. Well, it has helped to achieve extraordinary advances in these areas
In recent years, deep learning has become one of the most in-demand fields in computing. Well creates computational models composed of various layers of processing. In which networks can create levels of abstraction to represent data.
He has made great contributions to the discovery of new drugs and detection of diseases, among others. Helping to increase knowledge in biology, immunotherapy and other areas of modern science.
What is deep learning or Deep Learning?
Deep learning, also known as deep learningIt is a type of learning automatic and . East imitates in an extraordinary way the way in which humans obtain some knowledge.
Definitely, deep learning is a key element in data sciencesince it offers benefits for the Scientific advances. Taking into account that they have the task of collecting, analyzing and interpreting a lot of information. Thanks to deep learning, this process is much faster and easier.
How does deep learning work?
Deep learning has its process. Each algorithm in the hierarchy applies a nonlinear transformation to your input, using what it learns to create a statistical model. This process is supervised, and for this, the programmer must be specific and tell the computer the type of things that he must do.
This laborious process is called feature extraction, and the success rate of the computer Depends on the ability of the programmer. This being the one that should define the set of specific characteristics. The great advantage of deep learning is that it creates a set of functions autonomously and without supervision. Which makes the process faster, more efficient and more precise.
To achieve an acceptable level of accuracy, deep learning programs require access to a large amount of training and processing data. And none are available to programmers in the big data and cloud computing era. Because deep learning creates statistical models from its interactive output, it can also create accurate predictive models based on structured data.
Utility and applications of deep learning
The Applications that belong to deep learning they may seem a bit unreal to those who have not approached this area. But if you are familiar with this topic, Do you understand the potential of each of them?.
Understanding that they are truly important and very useful applications:
Automatic translation
Neural networks are very useful in the analysis and identification of images, since they have visible letters. When these are identified, can be transformed into text until it is translated. This application involves an automatic translation into another language, with words, sentences and phrases in another specific language.
Despite the fact that translation has been around for many years, deep learning is achieving extraordinary results in the following areas:
- Automatic image translation
- Automatic translation of texts
To translate a text, no pre-processing necessary. This allows the algorithm to learn the dependencies between the words and assign them to a new language.
Healthcare
Deep Learning is very useful in the early, fast and accurate diagnosis of terminal illnesses. It helps to optimize the results of the treatments of pathologies and the standardization of the course of said treatments. The use of deep learning helps mitigate the risks of diseases, and in turn reduces costs. The use of deep learning It is quite wide, and you can get the most out of it. There is no doubt, yesu use makes your work easier.
Deep learning methods What are they and what are their advantages?
exist several useful methods for building deep learning modelsThey are strong enough. These techniques take into account the decrease in the learning rate, learning by transfer, training from scratch and desertion.
Next, you will learn about these deep learning methods and their wonderful advantages:
Learning rate decay
The learning rate is considered as a factor that defines the system or that establishes the ideal conditions for its operation. This method controls the change that the model undergoes in response to the estimated errors each time the weights of the model are modified. When learning rates are high, training processes can be unstable. While small learning rates produce a long training process.
The method of slowing down the learning rate is also known as adaptive learning rate. It consists of adapting the learning rate to increase performance and reduce training time. The easiest adaptations include ideal techniques to reduce the rate of learning over time.
transfer learning
This process involves the refinement of a model that has been previously trained. Likewise, requires an interface to the interior of a pre-existing network. In this method, users feed the existing network with new data, which contains unknown classifications. When making settings to the network, new tasks can be performed with a specific categorization capacity.
The The advantage of this method is that it requires less data than others. what reduces calculation time just hours, and even minutes.
train from scratch
For this method, requires a developer to collect a pre-tagged data set. In addition to configuring a network architecture that is capable of learning the characteristics and models. This method is quite useful, especially for new applications, even if they contain a large number of output categories. Being a less common approach, it requires large amounts of data, causing training to take days or even weeks.
Quit
This is a method that tries solve the problem of overfitting in networks with a larger number of parameters. It does this by randomly dropping the units and their connections from the neural network during the training process. It is proven that this method can considerably improve the performance of neural networks in various supervised learning tasks. Among which includes speech recognition, document classification and computational biology.
Using these incredible deep learning methods provides you with unbeatable advantages such as:
- Ability to work based on a larger amount of data
- Develop predictions from the data already analyzed
- Apply and discover new knowledge
Deep Learning and artificial intelligence Is it possible to create a neural network?
Deep Learning tries to mimic the human brain, continuously analyzing data from a given logical structure. To do these analyses, the Deep Learning is based on its artificial neural networks.
Neural networks identify patterns and classify them into different types of information. The different layers of the neural networks are used as filters. Going from the general elements to the more subtle ones and increasing the probability of detecting and generating correct results. It is because of that When a Deep Learning system must recognize an object, what it does is compare it with what it already knows.
For example, if you ask three people to write the letter “A” in capital letter, you will notice that each one has a different handwriting. Therefore they will write it differently even if it is the same letter. A normal computer cannot do this, but using Deep Learning technology yes you can do it. This is because it captures the image of each letter “A” written, checks the pixels of each photo, and thus recognizes the written text. Deep Learning is based on the use of artificial neural networks.
Next, you will know the three most used types:
Convolutional Neural Networks
Called CNNs, convoluted neural networks are nothing more than neural networks designed to process images. This means that they are in charge of classifying images based on the patterns and objects that appear in them. For example lines, circles and even eyes and faces.
CNNs are able to operate with raw images, without prior processing. This makes them very useful for visual image classification applications., and natural language processing. Since they allow you to identify the types of email, that is, if they are financial, commercial or of any other nature.
Recurrent Neural Networks
Called RNN, recurrent neural networks use sequential or time series data to solve ordinal or temporal problems. For example, language translation, speech recognition, natural language processing, and image capture.
Neural networks are found in technologies like Siri and Google Translate. In them, natural language processing recognizes a person’s speech. Distinguishing if the person who is speaking is a man or a woman, an adult or a minor and even his accent. Thus, It is possible to analyze the person’s way of speaking and thus be able to arrive at their idiolect.
recurrent neural networks differ from artificial neural networks in that they have memory. This means that the RNNs take information from previous inputs, ideal for influencing current inputs and outputs. An example is that when writing with the mobile, the keyboard displays a series of words as suggestionsbased on what is written. These suggestions are based on the characters that have been previously written.
Generative Adversarial Networks
Called GANs, antagonistic generative networks use two artificial neural networks and oppose them from each other. It is precisely for this reason that they are known as antagonistic. In this sense, one of the networks generates and the other network functions as discriminatory. Well it has been trained to recognize real content and act as a censor so that a network that generates content seems real. For this reason, these types of networks are used to generate images, voices and videos.
Problems and limitations of Deep Learning Is it viable?
These technologies, like others, have some limitations. Deep learning makes machines not dependent on humans, but learn automatically. But these systems are still far from perfectly imitating the human brain. In recent years, the neural networks that have been developed through Deep Learning, they have been very successful. This is because they can solve various problems and do tasks that…
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