Recently, machine learning and soft computing approaches have given rise to deep learning (DL) algorithms. DL is now a need due to its intelligence, effective learning, precision, and resilience in model development. Deep learning algorithms are the mathematical and computational methods to train and optimize deep learning models. These algorithms are responsible for the learning process where the model adjusts its parameters to minimize the error between predicted and actual outputs.
The primary algorithm used in deep learning is called backpropagation. It is a form of gradient descent that calculates the gradient of loss function concerning the model parameters and adjusts them accordingly to minimize the error. The algorithm iteratively updates the parameters in the opposite direction of the gradient, taking small steps towards optimal values.
DL methods refer to the various approaches used in DL to train, optimize, and apply deep neural networks. These methods encompass a range of algorithms, architectures, and tools that enable the development and deployment of models.
Supervised Learning: This method involves training deep learning models using labeled data where the input data and corresponding desired outputs are provided. The models learn to map inputs to outputs by minimizing the error between predicted and actual outputs. Common algorithms used in supervised learning include backpropagation and gradient descent.
Unsupervised Learning:
Unsupervised learning methods aims to discover pattern or representation in unlabeled data that do not rely on explicit output labels and focus on capturing the underlying structure or distribution of data. Generative models like variational autoencoders (AE) and generative adversarial networks (GAN) clustering algorithms are commonly used in unsupervised DL.
Semi-Supervised Learning: Semi-supervised learning is a method that lies between supervised and unsupervised learning approaches. The basic idea behind unlabeled data can provide valuable information about the underlying data distribution and structure, aiding in learning better representations or features. This learning has proven beneficial when obtaining labeled data is expensive, time-consuming, or impractical. It allows for leveraging the abundance of unlabeled data available in many real-world applications.
Gradient Descent: Gradient descent is the fundamental optimization algorithm used in DL for calculating the gradients of the loss function for model parameters and updates the parameters in the direction that minimizes the loss. There are variations of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, which differ in the amount of data used to compute gradients at each iteration.
Batch Normalization: Batch normalization is a technique used to improve the training speed and stability of deep neural networks. It normalizes the input to a layer by subtracting the batch mean and dividing the standard deviation. Batch normalization helps alleviate the "internal covariate shift" problem, allowing the network to converge faster.
Activation Function: The activation function introduces non-linearity into DL models, allowing them to learn complex patterns and predictions. Common activation functions include the sigmoid function, hyperbolic tangent function, and rectified linear unit. Activation functions determine the output of a neuron based on its weighted sum of inputs.
Regularization Techniques: Regularization techniques prevent overfitting and improve the generalization ability of deep learning models. In addition to dropout, other regularization techniques, including L1 and L2 regularization, add a penalty term to the loss function based on the magnitude of the model parameters. These techniques encourage the model to learn simpler and more generalizable representations.
Dropout: Dropout is a regularization technique commonly used to prevent overfitting. During training, dropout randomly sets a fraction of neuron output to zero at each update, forcing the network to learn redundant representations. This helps to prevent the model from relying too heavily on any individual neuron or feature.
Learning Rate Scheduling: Learning rate scheduling adjusts the learning rate during training. It determines the step size taken during gradient descent updates. Scheduling methods such as reducing the learning rate over time or adapting it based on the validation loss help the model converge faster and perform better.
DL algorithms are most important for several reasons. They are described as,
Feature Learning and Representation: DL algorithms excel at automatically learning relevant features and representations directly from the data. Instead of relying on explicit feature engineering, it can learn intricate and abstract features optimized for specific tasks. This ability to automatically learn meaningful representations contributes to the high performance achieved by DL models across various domains.
End-to-End Learning: Deep learning algorithms enable end-to-end learning, where the model learns directly from raw data without requiring manual feature engineering. Instead of relying on handcrafted features, DL models learn hierarchical representations of data through multiple layers of neurons. This reduces the need for domain expertise and manual feature extraction, making it more accessible and efficient for multiple applications.
Scalability: This is highly scalable to large datasets and can effectively leverage the computational power of modern hardware. The parallel nature of computations allows efficient training and inference on massive amounts of data, making it suitable for big data applications.
Handling Complex and Unstructured Data: It is capable of handling and extracting useful information from unstructured, complex data types such as images, videos, text, and audio. Traditional ML algorithms may struggle to capture intricate patterns and representations in these data types.
State-of-the-art Performance: DL has achieved performance in various fields, including computer vision, speech recognition, and NLP. The ability to learn complex patterns and representations from large amounts of data and advancements in model architectures has propelled to surpass traditional ML methods in many challenging tasks.
Generalization: This is known for its ability to generalize well to unseen data. By learning hierarchical representations, DL models can capture underlying patterns and variations in the data, allowing us to make accurate predictions on previously unseen examples. This generalization capability is crucial for applications where the model needs to perform well on new and diverse data.
Convolutional Neural Network (CNN): One of the most well-known DL method architectures is CNN. This technique is generally employed for image processing applications. This presents three separate layer types: convolutional, pooling, and fully connected. The feed-forward and back-propagation stages are two training process phases in each CNN. ZFNet, GoogleNet, VGGNet, AlexNet, and ResNet are the most prevalent CNN designs.
Recurrent Neural Networks (RNNs):Speech, handwriting, text, sequences and other patterns can all be recognized using RNN technology. RNN benefits the structures cyclic connections, which use recurrent computations to analyze incoming data sequentially. A regular neural network has been stretched across time to create RNN by adding edges that feed into the subsequent time step rather than subsequent layers in the same time step. Each preceding input is saved in a state vector in hidden units, and the outputs are computed using these state vectors. The current uses of RNN applications include energy, hydrological prediction, economics, expert systems, navigation, and music genre recognition.
Radial Basis Function Network (RBFN): RBFN is a special type of neural network that follows a feedforward approach and uses radial functions as activation functions. They consist of three layers: input layer, output layer, and hidden layer, mainly used and focused for regression testing, classification, and time series forecasting. RBFN performs these tasks by measuring similarities present in the training data set. Usually, an input vector feeds this data to the input layer, confirming the identification by comparing it to the previous dataset and giving the result. The input layer has neurons that respond to this data, and nodes in the layer efficiently classify data classes. Neurons originally reside in the hidden layer but work closely with an input layer. The hidden layer contains a Gaussian transfer function inversely proportional to the output distance from the neurons center. The output layer has a linear combination of radially based data passing as a parameter to the neurons to generate the output.
Deep Belief Networks (DBNs): DBNs are used for learning data from high dimensional manifolds except for connections between units inside each layer, and this approach has many levels and connections between layers. Restricted Boltzmann machines (RBMs) are trained greedily and are found in DBNs. Every RBM layer interacts with both the layer before and after. This model comprises a feed-forward network and multiple layers of feature extractors called RBM. DBN is one of the most dependable DL techniques with great accuracy and computational efficiency. The DBN includes intriguing applications for a variety of technical and scientific issues. Time series forecasting, cancer diagnosis, renewable energy prediction, economic forecasting, and human emotion sensing have been among the public application domains.
Denoising AutoEncoder (DAE): As an asymmetrical neural network for extracting features from noisy datasets, DAE has been developed as an extension of AE. The input, encoding, and decoding levels are three primary layers of DAE. To take high-level characteristics, DAE can be aggregated. The DEA approach produces the Stacked Denoising AutoEncoder (SDAE), an unsupervised algorithm for nonlinear dimensionality reduction. With several hidden layers and a pretraining process, this approach is a sort of feed-forward neural network. Energy forecasting, cybersecurity, image classification, speaker verification, banking, and fraud detection are the current popular applications of DEA.
Multilayer Perceptrons (MLPs): MLPs comprise multiple layers of interconnected nodes known as neurons arranged feedforward. The neurons in one layer receive inputs that apply a weighted sum operation and pass the result through an activation function to generate an output. The output from one layer serves as the input to the next for creating a hierarchical structure that allows the network to learn complex representations. MLP is trained using supervised learning techniques such as backpropagation, which iteratively adjusts the weight and biases of neurons to minimize the error between predicted and actual outputs. With the advancement in DL, MLPs have been integrated into larger architectures and have proven to be a foundational building block for more complex models such as CNNs and RNNs.
Long Short-Term Memory (LSTM): As a general-purpose computer, LSTM is an RNN technique that advantages feedback links. This technique may be used for image processing, pattern identification, and sequence processing. The three main components of an LSTM are input, output, and forget gates. LSTM controls when to let input into the neuron and recall computations made in the previous time step. One of its key advantages is that the LSTM approach bases all decisions on the current input itself. In environmental applications, including geological modeling, hydrological prediction, air quality, and hazard modeling, LSTM has demonstrated considerable promise. The LSTM architecture has the potential to generalize, making it ideal for a wide range of application domains. The wind energy industry, solar power modeling, energy demand and consumption are the other application domains of LSTM.
Generative Adversarial Networks (GANs): GAN is a DL algorithm that generates new data instances that match the training data. GAN usually consists of two components.
GAN have become very popular because they are often used to sharpen astronomical images and simulate the lensing effect of gravitational dark matter. It is also accustomed in video games to enhance graphics by recreating 2D textures in higher resolutions such as 4K. They are used to render a human face and his 3D objects and create realistic cartoon characters.GANs work in simulations by generating and understanding fake and real data. During training to understand the data, the generator produces different types of fake data, and the discriminator quickly learns how to adapt the data and respond as fake data. Then, the GAN submits those recognized results for updating.
Restricted Boltzmann Machines (RBMs):RBMs are similar to probabilistic neural networks that learn from probability distributions on a given set of inputs. It is largely used in dimensionality reduction, topic modeling, regression and classification and is considered a building block of DBN. RBI consists of two layers: a visible layer and a hidden layer. Hidden units connect both and have bias units connected to the nodes that produce the output. RBM typically has two phases: forward pass and reverse pass.RBM takes the input and converts it to a number encoded in the forward pass. The RBM considers the weights of each input, takes the weights of inputs in a backward pass and transforms them into reconstructed inputs. Later, the two transformed inputs are combined with their weights. These inputs are forwarded to the visible layer, where activations are performed to produce outputs that can be easily reconstructed.
Self-Organizing Maps (SOMs):SOMs are used to revolutionize data visualization and understanding through artificial neural networks. They enable us to comprehend the dimensions of complex data that often elude human perception. By leveraging SOMs, we can effectively tackle high-dimensional data without succumbing to errors or limitations associated with manual analysis.At the core, SOM facilitates data visualization by initializing weights for numerous nodes and sampling random vectors from training data. These nodes scrutinize and assess the relative weights to unravel intricate dependencies. A victorious entity emerges among these nodes - the “Best Matching Unit (BMU)” . As SOMs progress, they continually explore and discover the winning nodes, gradually diminishing the number of nodes from the sample vector. Consequently, nodes closer to the BMU gain heightened significance as they hold the potential to unearth crucial patterns and contribute to subsequent analyses. One such compelling application of SOMs lies in the "RGB color combination" realm that permeates daily tasks. By employing SOM, unravel the intricate relationships between different color components, shedding light on the nuances and subtleties that define our visual experiences.