Let’s discuss the endless possibilities of deep learning and the top deep learning algorithms behind the popular deep learning applications like language recognition, autonomous vehicles, deep learning robots, etc.
Deep learning has achieved massive popularity in scientific computing, and its algorithms are helpful in various industries that solve complex problems. Each deep learning algorithm uses different types of neural networks to perform specific tasks.
While firing Siri or Alexa with questions, people usually wonder how machines deliver super-human accuracy. It is possible with deep learning – the amazingly intimidating area of data science.
Top deep learning algorithms explained
Deep learning makes use of artificial neural networks to deliver sophisticated computations on vast amounts of data. Therefore, it is a type of machine learning that acts based on the structure and function of the human brain.
Thus, deep learning algorithms train machines by learning from examples. Also, industries such as health care, eCommerce, entertainment, and advertising usually use deep learning.
Thus, it is a subset of artificial intelligence with networks competent of unsupervised learning from unstructured or unlabeled data.
Deep learning has risen hand-in-hand with the digital era. Therefore, this has caused a revolution in data extraction in all forms and from every region of the world. This data, renowned as big data, is drawn from sensational sources like social media, internet search engines, e-commerce platforms, and online cinemas.
While deep learning algorithms highlight self-learning representations, they depend upon ANNs that reflect how the brain computes information.
Therefore, during the training process, algorithms use unknown elements in the input distribution to obtain features, group objects, and discover functional data patterns.
Deep learning models use several algorithms. As no one network is considerably whole, some algorithms are highly suitable to perform specific tasks. Thus, it’s good to gain a solid understanding of all primary algorithms to choose the right ones.
A neural network is a web-like human brain consisting of artificial neurons, also known as nodes. Hence, these nodes are piled next to each other in three layers:
- The input layer
- The hidden layer(s)
- The output layer
Therefore, data provides each node with information in the form of inputs. Thus, the node doubles the inputs with random weights, calculates them, and adds a bias. Lastly, nonlinear functions, also known as activation functions, determine which neuron to fire.
Deep learning algorithms operate with almost all kinds of data and need large amounts of computing power and information to solve complicated problems. Now, let us deep-dive into the list of deep learning models.
CNNs, also recognized as ConvNets, consist of multiple layers mainly for image processing and object detection. Also, it can be called a deep learning algorithm for image processing.
Yann LeCun produced the first CNN in 1988 and was named LeNet. Hence, it is for recognizing characters like ZIP codes and digits.
Thus, CNNs are broadly used to identify satellite images, process medical images, forecast time series, and detect anomalies.
How Do CNNs Work?
CNN’s have various layers that process and extract features from data:
- CNN has a convolution layer that has different filters to perform the convolution operation.
- It also has a ReLU layer to execute operations on elements. The output is a revised feature map.
- The revised feature map next feeds into a pooling layer. Pooling is a down-sampling operation that lessens the dimensions of the feature map.
- The pooling layer then transforms the resulting two-dimensional arrays from the pooled feature map into a single, long, continuous, linear vector by flattening it.
- A fully connected layer forms when the flattened matrix from the pooling layer is served as an input, classifying and identifying the images.
LSTMs are a set of Recurrent Neural Networks (RNN) specialized in learning and memorizing long-term dependencies. Recalling past information for a long duration is the default behaviour.
LSTMs preserve information over time. Thus, they are helpful in time-series prediction as they remember previous inputs.
Therefore, LSTMs have a chain-like structure where four interacting layers communicate uniquely. Besides time-series predictions, LSTMs are typically for speech recognition, music composition, and pharmaceutical development.
How Do LSTMs Work?
- Firstly, they ignore irrelevant parts of the previous state
- Next, they selectively renew the cell-state values
- Ultimately, the output of certain parts of the cell state
RNNs have associations that form directed cycles, allowing the LSTM to be the inputs to the current phase. RNNs are usually for image captioning, time-series analysis, natural-language processing, handwriting recognition, and machine translation.
How Do RNNs work?
- The output of the LSTM becomes an input to the current phase, enabling the memory of past inputs due to its efficient internal memory.
- RNNs can process inputs of different lengths. The more the computation, the more are the chances of gathering information, and the model size does not grow with the input size.
GANs are generative deep learning algorithms that produce new data instances that relate to the training data. Therefore, GAN has two components: a generator, which generates fake data, and a discriminator, which learns from that phony information.
How Do GANs work?
- The discriminator gets the difference between the generator’s fake data and the actual sample data.
- Hence, during the initial training, the generator creates fake data, and the discriminator quickly learns to tell that it’s false.
- The GAN sends the output to the generator and the discriminator to renew the model.
RBFNs are unique types of feedforward neural networks that utilize radial basis functions as activation functions. Therefore, they have input, hidden, and output layers used for classification, regression, and time-series prediction.
How Do RBFNs Work?
RBFN utilizes trial and error to define the structure of the network. It has two steps as follows:
- Firstly, the centres of the hidden layer using an unsupervised learning algorithm are determined.
- Lastly, the weights with linear regression are determined.
MLPs is a great place to start learning about deep learning technology.
It belongs to the family of feedforward neural networks with various layers of perceptrons that have activation functions. Therefore, MLPs consist of an input layer and an output layer that is entirely connected.
They have an equal number of input and output layers but may have various hidden layers and can be used to build speech recognition, image recognition, and machine-translation software.
How Do MLPs Work?
- MLPs serve the data to the input layer of the network. The layers of neurons join in a graph so that the signal passes in one direction.
- It computes the input with the weights that exist between the input layer and the hidden layers.
- MLPs use activation functions to decide which nodes to fire.
- It also trains the model to know the correlation and learn the dependencies within the independent and the target variables of a training data set.
Professor Teuvo Kohonen invented SOMs, enabling data visualization to decrease data dimensions through self-organizing artificial neural networks.
Data visualization tries to solve the problem that humans cannot easily visualize high-dimensional data. Thus, SOMs help users know this high-dimensional information.
How Do SOMs Work?
- SOMs initialize weights for each node and pick a vector at random from the training data.
- They monitor each node to find which weights are the most suitable input vector. Therefore, the best node is knowingly the Best Matching Unit (BMU).
- SOMs find the BMU’s neighborhood, and the amount of neighbors decreases over time.
- Thus, the closer a node is to a BMU, the more its weight changes. The farther the neighbor is from the BMU, the less it learns.
DBNs are generative models that consist of various layers of stochastic, latent variables. Therefore, the latent variables have binary values and are usually called hidden units.
Deep Belief Networks (DBNs) are for image recognition, video recognition, and motion-capture data.
How Do DBNs Work?
- Greedy learning algorithms guide DBNs. The greedy learning algorithm practices a layer-by-layer approach for getting the top-down, generative weights.
- DBNs run the steps of Gibbs sampling over the top two hidden layers.
- Therefore, they draw a sample from the noticeable units using a single pass of ancestral sampling through the remaining model.
- DBNs discover that a single, bottom-up pass can infer the values of the latent variables in every layer.
RBMs are stochastic neural networks that can acquire from a probability distribution over a set of inputs.
Thus, this deep learning algorithm is for dimensionality reduction, classification, regression, collaborative filtering, and feature learning. RBMs constitute the building blocks of DBNs.
How Do RBMs Work?
RBMs consist of two layers:
- Visible units
- Hidden units
Every visible unit is symmetrically related to all hidden units. RBMs consists of a bias unit attached to all visible and hidden units but lacks output nodes.
Autoencoder is an unsupervised ANN that recognizes how to compress and encode data efficiently.
Then determines how to reconstruct the data back from the encoded compression to a representation that is as close to the initial input provided at first.
Hence, autoencoders should encode the image and then reduce the input size into a smaller entity. Lastly, the autoencoder decodes the image to generate the reconstructed image.
Deep learning has emerged over the past years, and deep learning algorithms have become broadly popular in many industries. Hence, it has made computers work smart and make them work according to one’s needs.
With the ever-growing data, these algorithms would only grow more efficient with time and would indeed be able to replicate the human brain.