This article was published as part of the Data Science Blogathon
Este artículo tiene como objetivo explicar el deep learningDeep learning, A subdiscipline of artificial intelligence, relies on artificial neural networks to analyze and process large volumes of data. This technique allows machines to learn patterns and perform complex tasks, such as speech recognition and computer vision. Its ability to continuously improve as more data is provided to it makes it a key tool in various industries, from health... y algunos algoritmos de aprendizaje profundo supervisados. This article will discuss the following topics
- Deep Learning Definition
- How the deep learning algorithm works.
- Types of Supervised Deep Learning Algorithms
- The 5 main applications of deep learning algorithms
Deep Learning Definition
Deep learning is a subset of a machine learning algorithm that uses multiple layers of neural networks to perform data processing and calculations on a large amount of data.. Deep learning algorithm is based on the function and functioning of the human brain.
Deep learning algorithm is capable of learning without human supervision and can be used for structured and unstructured data types. Deep learning can be used in various industries such as healthcare, the finances, to the bank, electronic commerce, etc.
How the deep learning algorithm works
El funcionamiento de los algoritmos de aprendizaje profundo depende de la red neuronalNeural networks are computational models inspired by the functioning of the human brain. They use structures known as artificial neurons to process and learn from data. These networks are fundamental in the field of artificial intelligence, enabling significant advancements in tasks such as image recognition, Natural Language Processing and Time Series Prediction, among others. Their ability to learn complex patterns makes them powerful tools.., just like the human brain calculates information using millions of neurons.
Let's analyze the type of layers:
- Input layerThe "input layer" refers to the initial level in a data analysis process or in neural network architectures. Its main function is to receive and process raw information before it is transformed by subsequent layers. In the context of machine learning, Proper configuration of the input layer is crucial to ensure the effectiveness of the model and optimize its performance in specific tasks....: the input layer has input features and a data set that we know.
- Hidden cloak: hidden layer, just like we need to train the brain through hidden neurons.
- Output layerThe "Output layer" is a concept used in the field of information technology and systems design. It refers to the last layer of a software model or architecture that is responsible for presenting the results to the end user. This layer is crucial for the user experience, since it allows direct interaction with the system and the visualization of processed data.... – value we want to classify
We get the input feature from the observation and put it on a layer. That layer creates an output that becomes the input for the next layer which is known as the hidden layer.. This happens until we get the final result.
We further separate the network and add many hidden layers depending on the complexity of the problem and we connect everything just like the human brain interconnects everything and that's how the input values are processed through all the hidden layers and then we have the output. That is why this learning process is known as deep learning because a lot of calculations are done between the input and output layers.
Types of deep learning algorithms:
Here is the classification of Deep Learning algorithms:
Basically, we can classify deep learning into two types and then go deeper into each type in various deep learning algorithms.
Here, in this article, we will discuss supervised deep learning algorithms.
- Red neuronal artificial
- Red neuronal convolucionalConvolutional Neural Networks (CNN) are a type of neural network architecture designed especially for data processing with a grid structure, as pictures. They use convolution layers to extract hierarchical features, which makes them especially effective in pattern recognition and classification tasks. Thanks to its ability to learn from large volumes of data, CNNs have revolutionized fields such as computer vision..
- Red neuronal recurrenteRecurrent neural networks (RNN) are a type of neural network architecture designed to process data streams. Unlike traditional neural networks, RNNs use internal connections that allow information from previous entries to be remembered. This makes them especially useful in tasks such as natural language processing, Machine translation and time series analysis, where context and sequence are central to the...
Now, let's analyze these 3 algorithms in brief:
1. Red neuronal artificial:
An artificial neural network is the component of a computer system designed in such a way that the human brain analyzes and makes a decision. Ann is the cornerstone of deep learning and solves the problem that seems impossible or very difficult for humans.
Artificial neural networks work like a human brain. The human brain has billions of neurons and each neuron is made up of a cell body that is responsible for calculating information by carrying the information to the hidden neurons and providing the final output..
ANN inicialmente en la fase de trainingTraining is a systematic process designed to improve skills, physical knowledge or abilities. It is applied in various areas, like sport, Education and professional development. An effective training program includes goal planning, regular practice and evaluation of progress. Adaptation to individual needs and motivation are key factors in achieving successful and sustainable results in any discipline.... aprende a identificar patrones basados en entradas dadas a la capa de entrada. During this phase, Ann's output is compared to the actual output, and the difference between these two is known as an error.
The goal is to minimize the error by adjusting the weight and bias of the interconnect, what is known as backpropagation. With the process of backpropagation, the difference between the desired output and the actual output produces the smallest error.
2. Red neuronal convolucional
CNN is a supervised type of deep learning, most preferably used in image recognition and computer vision.
CNN has multiple layers that process and extract important features from the image.. There are mainly 4 CNN operation steps
Paso: 1 Convolution operation with Relu activation function
The goal of the convolution operation is to find features in the image using feature detectors to preserve the special relationship between pixels.. The función de activación ReluThe ReLU activation function (Rectified Linear Unit) It is widely used in neural networks due to its simplicity and effectiveness. is defined as ( f(x) = max(0, x) ), meaning that it produces an output of zero for negative values and a linear increment for positive values. Its ability to mitigate the problem of gradient fading makes it a preferred choice in deep architectures.... se utiliza para romper la linealidad y desea aumentar la no linealidad porque las imágenes en sí mismas son altamente no lineales.
Paso: 2 grouping
Binning is a downsampling operation that reduces dimensions and computation, reduce el sobreajuste ya que hay menos parametersThe "parameters" are variables or criteria that are used to define, measure or evaluate a phenomenon or system. In various fields such as statistics, Computer Science and Scientific Research, Parameters are critical to establishing norms and standards that guide data analysis and interpretation. Their proper selection and handling are crucial to obtain accurate and relevant results in any study or project.... y el modelo es tolerante a la variación y la distorsión.
Paso: 3 flattening
El aplanamiento se utiliza para poner la salida de la agrupación en una matriz de dimension"Dimension" It is a term that is used in various disciplines, such as physics, Mathematics and philosophy. It refers to the extent to which an object or phenomenon can be analyzed or described. In physics, for instance, there is talk of spatial and temporal dimensions, while in mathematics it can refer to the number of coordinates necessary to represent a space. Understanding it is fundamental to the study and... antes de continuar con el procesamiento.
Paso: 4 fully connected layer
A fully connected layer is formed when the flattening output is fed to a neural network that further classifies and recognizes the images..
3. Recurrent neural networks (RNN)
RNN is a type of supervised deep learning where the output of the previous step is fed as input to the current step. RNN's deep learning algorithm is best suited for sequential data. The RNN is most preferably used in image captions, time series analysis, natural language processing, handwriting recognition and machine translation.
The most important feature of RNN is the Hidden state, that memorizes certain information about a sequence. There are mainly 4 steps of how RNN works.
- The output of the hidden state at t-1 was fed into the input at time t.
- In the same way, the output at time t was fed into the input at time t + 1.
- RNN can process input of any considerable length
- RNN computation depends on historical sequence data and model size does not increase with input size.
This way, RNN converts independent activations to dependent activations, thus reducing the complexity of incrementing parameters and remembering each previous output by giving each output as input to the next hidden layer.
The 5 main applications of deep learning algorithms
Then, shows some ways in which deep learning is used in various industries.
1. Computer vision
Computer vision relies primarily on image processing methods. Before deep learning, the best computer vision algorithm based on conventional machine learning and image processing yielded an error rate of 25%. But, when a deep neural network was used for image processing, the error rate was reduced to 16 percent, and now with the advancement in deep learning algorithms, the error rate was reduced to less than 4%.
2. Analysis and comprehension of texts
Text analysis consists of the classification of documents, sentiment analysis, automatic translation, etc. Recurrent Neural Networks are the most useful deep learning algorithm here, due to the sequential type of textual data.
3. Speech recognition
Speech recognition allows computers to process human speech into text.. Traditionally, speech recognition relies heavily on an important feature extraction process, but deep learning is working directly on raw data and training done on a large audio recording dataset.
4. Pattern recognition
Pattern recognition is the automated identification of patterns and regularities in data.. The data type can range from text, images to sounds or audio.
PayPal uses deep learning through H2O, a predictive analytics platform, to help prevent fraudulent purchase and payment transactions and
5. autonomous vehicles
The autonomous vehicle managed to collect data about its environment from various sensors, explain it and, based on the explanation, choose what actions should be taken. Deep learning allows us to learn to do work as efficiently as humans.
Thank you for reading! In my next article, I will explain various activation functions with applications.
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