Introduction
People frequently wonder how robots reach superhuman accuracy when they ask Siri or Alexa questions. Deep learning, the terrifying data science domain, is responsible for all this. This new area of deep learning technology is inspired by how neural networks work in the human brain. It has created intriguing artificial intelligence applications like language recognition, self-driving cars, and computer vision robots, to mention a few. Assume you’re intrigued by the unlimited potential of deep learning and want to learn more about the standard deep learning algorithms that power popular deep learning applications. Then this is the article for you.
From this article, you will know the top 10 deep learning techniques people are working on and how widely they are used worldwide to make apps, image processing, chatbots, and many more. So, from this article, you can learn how and for which purpose you can use each deep learning technique and how each method works. Below you can see each deep learning technique individually with its details.
Top 10 Deep Learning Techniques
Convolutional Neural Networks (CNN) in Deep Learning
CNNs, called ConvNets, are multilayer neural networks primarily used for image processing and object detection. Yann LeCun created the first CNN, known as LeNet, in 1988. It recognized characters such as ZIP codes and numbers from images. CNNs are commonly used to detect abnormalities in satellite photos, interpret medical imaging, forecast time series, and find anomalies.
CNNs have numerous layers that analyze and extract data features:
Layer of Convolution
To accomplish the convolution process, CNN contains a convolution layer with many filters.
Linear Unit Rectified (ReLU)
CNN features a ReLU layer that breaks any kind of linearity that might have existed in the image. This makes it easier for the model to detect features. The resulting image is called a feature map.
A layer of Pooling
The Pooling layer, like the Convolutional Layer, is in charge of shrinking the Convolved Feature’s spatial size. Lowering the dimensions will lower the amount of CPU power needed to process the data. Average pooling and maximum pooling are the two forms of pooling.
Classic Neural Network in Deep Learning
Classic Neural Networks, also known as Fully Connected Neural Networks, are distinguished by their multilayer perceptrons, which are connected to the continuous layer. Fran Rosenblatt, an American psychologist, created it in 1958. It entails converting the model into essential binary data inputs. This model includes two functions, which are as follows:
Linear function, as the name implies, is a single line that multiplies its inputs with a constant multiplier, and the classic neural network in deep learning is a nonlinear function composed of neurons that are connected in a network. The nonlinearity of the neurons allows for the representation of complex functions, which makes them suitable for deep learning tasks. Examples of Nonlinear functions are:
- The Sigmoid Curve is a function perceived as an S-shaped curve with a range of 0 to 1.
- Tanh (hyperbolic tangent) is an S-shaped curve from -1 to 1.
- ReLU (rectified linear unit): A single-point function returns 0 if the input is less than the specified value and the linear multiple if the information is more than the set value.
Long Short-term Memory Network Deep Learning
LSTMs are Recurrent Neural Network (RNN) types that can learn and remember long-term dependencies. The default habit is to recall prior knowledge over extended periods.
LSTMs store information over time. They are helpful in time-series prediction because they recall prior inputs. LSTM has a chain-like structure with four interconnected layers that communicate clearly. They are frequently used for speech recognition, music production, pharmaceutical research, and time-series forecasting.
LMTS follows these three steps to work:
- To begin, they need to remember unimportant aspects of the initial condition.
- Following that, they selectively update the cell-state values.
- Finally, the output of specific cell state components.
Recurrent Neural Network
At first, RNNs scientists created to aid sequence prediction; for example, the Long Short-Term Memory (LSTM) algorithm is well-known for its versatility. However, these networks are solely based on data sequences with varying input lengths.
The information gathered from its initial state is used as an input value for the current prediction by the RNN. So, it can aid in attaining short-term memory in a network, resulting in the successful management of stock price movements or other time-based data systems.
There are generally two types of RNN designs that help in issue analysis. They are as follows:
- LSTMs help predicts data in time sequences utilizing memory. There are three gates in it: input, output, and forget.
- Gated RNNs are also beneficial for time sequence data prediction using memory. There are two gates: Update and Reset.
A generative Adversarial Network
GAN is deep learning generative algorithm that generates new data instances that mirror the training data. GAN comprises two parts: 1st generator that learns to produce false data and a discriminator that learns from that incorrect information.
GANs have grown in popularity over time. They may be used to enhance astronomy photographs and imitate gravitational lensing for dark matter investigations. Video game producers also use them to upgrade low-resolution, 2D graphics in ancient games by reproducing them in 4K or greater resolutions using image training. GANs help create realistic pictures and cartoon characters, as well as photography of human faces and the rendering of 3D objects.
GAN follows these three steps to work:
- The discriminator learns to discriminate between bogus data generated by the generator and actual sample data.
- During the first training, the generator generates bogus data, and the discriminator quickly learns to recognize it.
- To update the model, the GAN delivers the results to the generator and discriminator.
Self-Organizing Maps
Self-Organizing Maps use unsupervised data to minimize the number of random variables in a model. As each synapse links to its input and output nodes, the output dimension is set as a two-dimensional model in this deep-learning approach.
The SOM adjusts the weight of the nearest nodes or Best Matching Units as each data point competes for its model representation (BMUs). The weights value fluctuates depending on how close a BMU is. Because consequences are regarded as a node attribute in and of themselves, the value signifies the node’s placement in the network.
SOM works by following these steps:
- SOMs use a vector at random from the training data to initialize weights for each node.
- SOMs analyze each node to determine which weights are most likely to be the input vector. The winning node is called the Best Matching Unit (BMU).
- SOMs find the BMU’s neighborhood, and the number of neighbors gradually decreases.
- SOMs give the sample vector a winning weight. The weight of a node varies as it gets closer to a BMU.
- The greater the distance between the neighbor and the BMU, the less it learns. For N iterations by SOMs step two is repeated.
Boltzmann Machine
Boltzmann Machine nodes are connected in a circular pattern because this network architecture has no set direction. Due to its uniqueness, this deep learning approach generates model parameters.
The Boltzmann Machines model is stochastic, unlike all preceding deterministic network models.
It works best for:
- System Surveillance
- Establishment of a binary recommendation platform
- Analyzing certain datasets
Radial Basic Function Networks
RBFNs are feedforward neural networks using radial basis functions as activation functions. They are commonly for classification, regression, and time-series prediction and have an input layer, a hidden layer, and an output layer.
How does it work?
- RBFNs classify input by assessing its similarity to samples from the training set.
- The input vector of RBFNs feeds the input layer. Therefore, they have an RBF neuron layer.
- The function computes the weighted sum of the inputs, and the output layer has one node for each data category or class.
- The neurons in the hidden layer have Gaussian transfer functions, which have inversely proportional outputs to the distance from the neuron’s center.
- The result of the network is a linear combination of the radial-basis functions of the input and the neuron’s parameters.
Deep Reinforcement Learning
Before delving into the Deep Reinforcement Learning approach, remember that reinforcement learning refers to the process by which an agent connects with its environment to change its state. For example, the agent may watch and respond appropriately. The agent assists a network in achieving its goal by engaging with the circumstance.
There is not only an input layer and an output layer but also numerous hidden multiple layers in this network architecture, where the state of the environment is the input layer itself. The approach is based on continual attempts to anticipate the future payoff of each action made in a particular scenario.
DRL works on
- Board games such as chess and poker
- Autonomous Vehicles
- Robotics
- Inventory Control
- Asset appraisal is an example of a financial task.
Multilayer Perceptron
To start learning about deep learning technologies MLPs are a great way. They are a feedforward neural network with many layers of perceptrons with activation functions. MLPs consist of a wholly coupled input and output layer. They contain the same input and output layers but contain several hidden layers and to develop voice, picture, and machine translation software.
How does it work?
- MLPs deliver data to the network’s input layer. The neurons are connected in a graph so that the signal only travels one way.
- MLPs calculate input based on the weights between the input and hidden layers.
- To decide which nodes to fire, MLPs employ activation functions. ReLUs, sigmoid functions, and tanh are examples of activation functions.
- MLPs use a training data set to train the model to comprehend the correlation and learn the dependencies between the independent and target variables.
Conclusion
In this article, we have discussed only the top 10 deep learning techniques, but there are several more deep learning approaches, each with its functions and practical approach. Each of these techniques is used by the developer for individual purposes. But to start your deep learning career, knowing the methods we mentioned in this article is an excellent start, as this article covers almost all the essential and trendy techniques developers use nowadays. To learn more information about deep learning, AI, or even machine learning, you can the other articles regarding these topics on our website.