## Deep Learning Algorithms## What is Deep Learning Algorithm?Deep learning can be defined as the method of machine learning and artificial intelligence that is intended to intimidate humans and their actions based on certain human brain functions to make effective decisions. It is a very important data science element that channels its modeling based on data-driven techniques under Deep learning algorithms are dynamically made to run through several ## Importance of Deep LearningDeep learning algorithms play a crucial role in determining the features and can handle the large number of processes for the data that might be structured or unstructured. Although, deep learning algorithms can overkill some tasks that might involve complex problems because they need access to huge amounts of data so that they can function effectively. For example, there's a popular deep learning tool that recognizes images namely Deep learning algorithms are highly progressive algorithms that learn about the image that we discussed previously by passing it through each neural network layer. The layers are highly sensitive to detect low-level features of the image like However, if we talk out the simple task that involves less complexity and a data-driven resource, deep learning algorithms fail to generalize simple data. This is one of the main reasons deep learning is not considered effective as Having said that, let's look understand some of the most important deep learning algorithms given below. ## Deep Learning AlgorithmsThe Deep Learning Algorithms are as follows: ## 1. Convolutional Neural Networks (CNNs)CNN's popularly known as CNNs process the data by passing it through multiple layers and extracting features to exhibit convolutional operations. The ## 2. Long Short Term Memory Networks (LSTMs)LSTMs can be defined as LSTM work in a sequence of events. First, they don't tend to remember irrelevant details attained in the previous state. Next, they update certain cell-state values selectively and finally generate certain parts of the cell-state as output. Below is the diagram of their operation. ## 3. Recurrent Neural Networks (RNNs)Recurrent Neural Networks or RNNs consist of some directed connections that form a cycle that allow the input provided from the LSTMs to be used as input in the current phase of RNNs. These inputs are deeply embedded as inputs and enforce the memorization ability of LSTMs lets these inputs get absorbed for a period in the internal memory. RNNs are therefore dependent on the inputs that are preserved by LSTMs and work under the synchronization phenomenon of LSTMs. RNNs are mostly used in captioning the image, time series analysis, recognizing handwritten data, and translating data to machines. RNNs follow the work approach by putting output feeds ## 4. Generative Adversarial Networks (GANs)GANs are defined as deep learning algorithms that are used to generate new instances of data that match the training data. GAN usually consists of two components namely a GANs work in simulation by generating and understanding the fake data and the real data. During the training to understand these data, the generator produces different kinds of fake data where the discriminator quickly learns to adapt and respond to it as false data. GANs then send these recognized results for updating. Consider the below image to visualize the functioning. ## 5. Radial Basis Function Networks (RBFNs)RBFNs are specific types of neural networks that follow a feed-forward approach and make use of radial functions as activation functions. They consist of RBFNs do these tasks by measuring the similarities present in the training data set. They usually have an input vector that feeds these data into the input layer thereby confirming the identification and rolling out results by comparing previous data sets. Precisely, the input layer has ## 6. Multilayer Perceptrons (MLPs)MLPs are the base of deep learning technology. It belongs to a class of feed-forward neural networks having various layers of The working of MLPs starts by feeding the data in the input layer. The neurons present in the layer form a graph to establish a connection that passes in one direction. The weight of this input data is found to exist between the hidden layer and the input layer. MLPs use activation functions to determine which nodes are ready to fire. These activation functions include ## 7. Self Organizing Maps (SOMs)SOMs were invented by SOMs help in visualizing the data by initializing weights of different nodes and then choose random vectors from the given training data. They examine each node to find the relative weights so that dependencies can be understood. The winning node is decided and that is called ## 8. Deep Belief Networks (DBNs)DBNs are called generative models because they have various layers of latent as well as stochastic variables. The latent variable is called a DBNs are powered by ## 9. Restricted Boltzmann Machines (RBMs)RBMs were developed by The functioning of RBMs is carried out by accepting inputs and translating them to numbers so that inputs are encoded in the forward pass. RBMs take into account the weight of every input, and the backward pass takes these input weights and translates them further into reconstructed inputs. Later, both of these translated inputs, along with individual weights, are combined. These inputs are then pushed to the visible layer where the activation is carried out, and output is generated that can be easily reconstructed. To understand this process, consider the below image. ## AutoencodersAutoencoders are a special type of neural network where inputs are outputs are found usually identical. It was designed to primarily solve the problems related to unsupervised learning. Autoencoders are highly trained neural networks that Autoencoders constitute three components namely the ## SummaryIn this article, we mainly use deep learning and the algorithms that work behind deep learning. First, we learned how deep learning changes the work at a dynamic pace with vision to create intelligent software that can recreate it and function like a human brain does. Later in this article, we learned some of the most used deep learning algorithms and learned the components that drive these algorithms are. Usually, to understand these algorithms, a person needs high clarity with mathematical functions discussed in some of the algorithms. These functions are so crucial that the working of these algorithms mostly depends on the calculations done by using these functions and formulae. An aspiring deep learning engineer knows all of these algorithms, and it is highly recommended for beginners to understand these algorithms before moving ahead into artificial intelligence. Next TopicKeras |