Deep learning is a subfield of machine-learning algorithms inspired by brain structure and function called artificial neural networks (ANN).
Deep learning (deep neural network) uses multiple layers between input and output layers to progressively extract high-level features.
A deep neural network (DNN) finds the right mathematical processing to convert inputs into outputs, whether this is a linear or non-linear relationship.
|Deep learning (DL) is a branch of Machine Learning (ML) based on artificial neural networks (ANN)|
What is Deep Learning (Deep Neural Network) and How Does Deep Learning Work?
What is Deep Learning?
Deep Learning (DL) is a very important branch of Machine Learning (ML). In deep learning, a computer model tests logarithms and programs and learns to improve and develop them on its own.
Deep learning is primarily concerned with developing algorithms that enable a computer to perform classification tasks directly from images, texts, and audios or perform difficult tasks that require a deep understanding of the data and the nature of its work.
Deep Learning is sometimes referred to as Deep Neural Networks, as it relies on Artificial Neural Networks.
Deep learning models can achieve sophisticated accuracy that sometimes exceeds human performance, and models are trained using a wide range of disaggregated data and neural network structures that contain multiple layers.
The characteristics of deep learning algorithms are their ability to learn and automate tasks without explicit programming.
Explicit programming here means writing certain commands and conditional tools to test data in order to arrive at a particular result or extract data features by data scientists.
Deep learning algorithms can automatically extract the most important features and patterns of repetitive data by looking at a lot of input data and then analyzing it to find direct or indirect links between the input data and the desired output.
Today, the uses of deep learning technology include all kinds of big data analytics applications, especially those focus on language translation, natural language processing (NLP), stock market trading, and Forex trading signals, medical diagnostics, image capture, and network security.
Deep learning algorithms are in contrast to machine learning algorithms that require a deep understanding of data and a great deal of effort to derive its features and patterns manually by data scientists.
Machine Learning (ML) is a current application of AI-based technology that gives systems the ability to automatically learn from experience and improve the programs and algorithms without being explicitly programmed.
Deep learning is a special type of machine learning that relates to algorithms inspired by the human (animal) brain called artificial neural networks (ANN).
Until now, most machine learning algorithms (such as Support Vector Machine and Logistic Regression) were dependent on data scientists' intervention to derive data features and patterns before they were consumed by these algorithms.
These algorithms learn by monitoring a large number of situations and focusing on pre-set patterns and feature manually, in addition to the fact that these algorithms depend on the intervention of data scientists.
The architecture of these algorithms is superficial and contains at most two layers of nonlinear transformations.
Although these algorithms succeeded in solving many problems with simple structures, they were not effective in solving problems with complex structures such as natural languages, visual scenes, and audio signals.
These problems require a deep understanding of the data and its patterns and the making of numerous and complex nonlinear transformations in order to convert the data in its natural form, such as the image, to the desired output, such as the description of the input image.
For this, deep learning technology emerged in the world.
How Do Machine Learning Algorithms Work?
Most machine learning algorithms rely on two basic steps: observation and simulation (prediction).
This is in a group of algorithms that rely on supervised learning or learning by observing previously known outcome events.
Machine learning algorithms first monitor the input data and try to derive distinct patterns and characteristics of the data and then simulate the behavior of functions based on the connections and relationships formed by observing the process of converting input data into specific outputs.
Here we distinguish between the term machine learning algorithms and machine learning models.
The algorithms are the necessary steps and methods used in the learning process and a model is a code structure that uses an algorithm to perform a particular task.
For example, deep learning algorithms can accurately predict house prices after looking at a large number of homes, their features, and prices, so the algorithm can automatically detect the most important features that determine the price of a house.
These features and patterns are then used to predict the prices of other houses based on the characteristics of each house.
Therefore, the more cases monitored during the model training process, the more likely and accurate the forecast results will be improved.
Deep Learning Architectures
The most popular deep learning architectures include convolutional neural networks (CNNs), deep stacking networks (DSNs), recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent unit (GRU).
How Does Deep Learning Work?
Deep learning algorithms consist of a set of non-linear processes with deep structures (hence called deep learning).
What distinguishes the architecture of these algorithms is that they are inspired by the neural networks in the human brain.
Therefore, Deep learning architectures are also called artificial neural networks.
In deep learning algorithms, the learning process takes place in two ways.
Supervised Learning: The machine learning process is marked based on a set of predefined data with the correct result, such as predicting house prices after reviewing a large number of nearby house prices.
Unsupervised Learning: The machine learning process is done using a set of data but without recognizing the correct result in advance, such as grouping data points based on a pattern automatically detected by an algorithm.
In deep learning, both supervised learning and unsupervised learning may be used to train the Artificial Intelligence (AI).
Neural network architecture (we mean artificial neural networks) is generally composed of three types of layers:
Input Layer: This is responsible for naturally entering data into the neural network after modeling.
Output Layer: This is responsible for issuing the final result of the neural network.
Hidden Layer: Between the input layer and the output layer there are a number of layers that convert the input data to the desired output, these layers are called hidden layers.
The word "hidden" implies that these layers are invisible to external systems and they are "private" to the neural network.
Some, therefore, refer to this type of algorithm as the “black box”.
Hidden layers perform calculations and computations on weighted inputs and produce net inputs which are then applied to produce actual outputs through an activation function.
There may be zero or more hidden layers in an artificial neural network.
Each hidden layer contains a certain number of neurons that receive data from the neurons of the previous layer and then perform a nonlinear mathematical operation and send it to the next layer, and so on until they reach the last layer (output layer).
Each neuron in the neural network has a multiplicative coefficient called "weight" and is used during the learning process to determine the importance of each feature of the input data.
The core of the learning process is to repeat the prediction and then reset the network weights to improve the results of the prediction process.
The goal is to find the set of weights that lead to the best possible prediction result.
Therefore, we want to note here that deep learning models, after the learning process, are only matrices of weights that are used to convert the input data to the output data by multiplying each feature of the input data with the weights reached during the learning process.