Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.
Here’s our simplified explanation of deep learning algorithms, a list of the top deep learning algorithms, along with the benefits and drawbacks of each.
What Are Deep Learning Algorithms?
Deep Learning is a subfield of machine learning, which is a type of artificial intelligence (AI). When a machine can perform tasks that would normally require human intelligence, that’s AI. When a machine can learn by experience through running and analyzing data and acquire skills without human input or explicit coding, that’s machine learning.
Deep learning is a kind of machine learning that works using algorithms that are inspired by the biological structure of the human brain, and they function in a similar way to the human brain. The algorithms are composed of artificial neurons called nodes, which are connected through weblike structures, known as artificial neural networks (ANN).
Also referred to as deep structured learning or differential programming, the goal of deep learning is to train the computer to develop human-like instincts by getting it to observe patterns, predict behavior and make decisions.
The machine’s learning deepens as it encounters new experiences and discovers new levels of data to explore. It’s all based on three layers — input level, hidden level and output level. And it’s all dependent on inputting data, analyzing it and learning from it.
There are many different types of deep learning algorithms with a variety of functionality and advantages. Let’s explore some of the most commonly used today.
Convolutional Neural Network (CNN/ConVet)
This algorithm is able to differentiate one image from another by taking in the input image and assigning learnable weights to aspects of the image. It then applies relevant filters using a structure inspired by the human brain’s visual cortex.
Benefits: Can automatically detect which features of an image are important without any human supervision, it can classify images more accurately than a human, and it operates in a computationally efficient manner.
Drawbacks: Algorithms don’t encode the orientation or position of an object, meaning the input data creates a set point that then requires a significant amount of training data to get up to speed.
Long Short Term Memory Networks (LSTM)
LSTM is a complex deep learning algorithm that focuses on sequence prediction and is able to learn order dependence. The inputs are not fixed, so it must learn and use context to make predictions. LSTMs are commonly used in language modeling, speech recognition and machine translation applications.
Benefit: Having such a large range of data parameters means there’s no need to make fine adjustments as you go.
Drawbacks: LSTMs need a lot of time and resources to get trained and ready for real-world application and they can be inefficient.
Generative Adversarial Networks (GAN)
Generative Adversarial Networks involve a type of machine learning modeling that automatically discovers and learns the patterns and regularity of input data so it can output new plausibilities concluded from the original dataset. Also, it can create a discriminator model that classifies objects as real or fake, meaning generated.
Common GAN applications include creating a new image from an existing one, such as new human faces, facial aging, photo blending and translating photos to emojis.
Benefit: Minimal need for direct data example inputs.
Drawback: Can require a lot of training, making GANs difficult to implement.
Multilayer Perceptron Neural Networks (MLPs)
MLPs have multiple layers of non-linear activation with training that occurs through a supervised learning technique called backpropagation, creating mathematical models that utilize regression analysis. It’s most commonly used for basic operations, such as data visualization, data compression or encryption.
Benefits: Works well with large sets of input data, provides fast predictions after training and can be applied to complex, non-linear problems accurately.
Drawback: Computations can be complex and time-consuming and the quality of the training determines the proper functioning of the model.
Radial Basis Function Networks (RBFN)
A radial basis function network uses radial basis as activation functions and learns to classify by measuring how similar the input is to examples within its training set that serve as a prototype.
RBFN is used for applications such as time series prediction, interpolation, function approximation and classification. It can support functions such as data anomaly detection, fraud detection and predicting stock prices.
Benefits: Relatively easy to design, has a strong tolerance to input noise, allows for quick training and has the ability to create robust predictions.
Drawback: Slow classification as each node must compute the RBF function.
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