Deep Learning Algorithms You Should Know About

In today's big-data ecosystem, users generate massive amounts of data 24/7. This data is processed using high-performance computing (HPC). At the same time, the parallel processing architecture of in-memory computing (IMC) powers the learning framework to update without impacting performance continually. Companies are leveraging these new scaling capabilities, achieved within the past few years, to enable more extensive and more powerful training models in artificial intelligence (AI). As more industries keep achieving impressive results, the number of applications for AI will continue to rise.

Classic machine learning (ML) algorithms work well for datasets with up to a few hundred characteristics. Many new technologies, like image recognition, are a different beast. A single 1200x1000 pixel RGB image, for example, has 36 million qualities. Taking a classic ML approach to such datasets is not only cumbersome but often not feasible. 

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How Deep Learning Works

how-deep-learning-works

How Deep Learning Gets Better Results

Deep learning uses layers of neural-network algorithms to decipher higher-level information at other layers based on raw input data. For example, in an image-recognition application, one layer could identify features such as sharp edges or contrasts in light, while another could identify how different distinct shapes appear. Further, a third layer could decipher what the image is showing. This is all achieved by learning the different ways information from previous layers are pieced together to form distinguishable objects.

Neural-network algorithms are designed to recognize data patterns based on an early understanding of how the human brain functions. Neural networks can help cluster points within a large sample of data based on the similarities of its features, classify data based on labels from previous data, and extract distinct features from data. The numerical patterns these networks recognize are stored in vectors that depict real-world inputs. Deep neural networks can be thought of as components of larger machine-learning applications involving algorithms for reinforcement learning, classification, and regression.

Deep learning uses self-taught learning and algorithm constructs with many hidden layers, big data, and powerful computational resources. The algorithmic framework is called the neural network, while the hidden layers in the network give it the moniker of deep learning.

The Google Brain Team project and deep learning software like TensorFlow have given further traction to the development of deep learning techniques. Such techniques are based on mathematical functions and parameters for achieving the desired output.

Deep Learning Architecture

Deep learning architecture is applied to social network filtering, fraud detection, image and speech recognition, audio recognition, computer vision, medical image processing, bioinformatics, customer relationship management, and many more fields. Deep learning models are everywhere, and the teams capable of training neural networks to deliver impressive results are among the most sought-after professionals today. Big data analytics as a field has slowly evolved to include deep learning expertise as not only a valuable addition but also a core and necessary skill set.

The Artificial Neural Network (ANN)

The concept of deep learning is modeled on behavioral patterns in the layers of neurons in the neocortex of the human brain. Generally, the more layers that exist, the deeper the model is, and the higher the performance.

simple-neural-network

A simple Neural Network (NN)

A neural network is a composition of perceptrons that are connected in different ways and that operate on different activation functions. A perceptron is an algorithm used in supervised learning of binary classifiers. A binary classifier is a function that decides whether an input (represented as a vector of numbers) belongs in one of two classes.

A network of perceptrons is called a multilayer perceptron, which is also referred to as an artificial neural network (ANN).

The deep learning architecture used today is primarily based on ANNs that utilize multiple layers of nonlinear processing for feature extraction and transformation.

Deep Learning Algorithms 

deep-learning

How Deep Learning Algorithms Work

While deep learning algorithms feature self-learning representations, they depend upon ANNs that mirror the way the brain computes information. During the training process, algorithms use unknown elements in the input distribution to extract features, group objects and discover useful data patterns. Much like training machines for self-learning, this occurs at multiple levels, using the algorithms to build the models.

Deep learning models make use of several algorithms. While no one network is considered perfect, some algorithms are better suited to perform specific tasks. To choose the right ones, it’s good to gain a solid understanding of all primary algorithms.

Here are some important ones used in deep learning architectures:

1. Multilayer Perceptron Neural Network (MLPNN) 

What it is: The multilayer perceptron serves as a solid introduction to deep learning. It uses a feed-forward supervised learning algorithm with up to two hidden layers to generate a set of outputs from a given set of inputs. As the name suggests, it is composed of more than one perceptron.

How it works: The network connects multiple layers of neurons in a directed graph so that the signal passes through the nodes in one direction. The output vector is computed given the inputs and a random selection of weights in the feed-forward computational flow. The model is trained to learn the correlation or dependencies between the input and output from a training data set. The error quantity between what should be the output for a given input is computed, and training involves tuning the weights and biases to reduce error at the output layer. The process is repeated for hidden layers going backward. Backpropagation is used to make the weight and bias adjustments relative to the error. The error itself can also be measured in a variety of ways, including by root-mean-squared error (RMSE).

Benefits: MLPNN’s can classify non-linearly separable data points, solve complex problems involving several parameters, and handle data sets with a large number of features, especially non-linear ones.

Use cases: MLPNN is used to solve problems that require supervised learning and parallel distributed processing, as in the following instances:

  • Image verification and reconstruction
  • Speech recognition 
  • Machine translation
  • Data classification
  • E-commerce, where many parameters are involved

2. Backpropagation

What it is: The Backpropagation algorithm is the foundation of neural network training.  The supervised learning algorithm computes a gradient descent with the weights updated backward — from output toward input — or backpropagation. 

How it works: Initially, a neural network consists of weights and biases that are poorly calibrated to read data. A neural network’s interpretation of data and the physical world is done through the values of its weights and biases.  Therefore, a poorly calibrated neural network implies a poor model. Whatever errors exist at the final prediction layer are sent back through the network to adjust the weights and biases so that future predictions have lower error values.

The algorithm calculates each neuron’s error contribution using a technique called the delta rule, or gradient descent optimization. The weight of neurons is adjusted to reduce the error at the output layer. Gradient descent implies a rate of change of a target marked as Y for change in a parameter marked as X. In this problem, Y would be the error produced in the neural network prediction, and X would represent various parameters in the data. Because there's more than one parameter, partial derivatives are used for each parameter. Also, because the layers of neural networks operate sequentially, finding the derivatives at each layer establishes a relation of the change of error at each layer for parameters in comparison to its previous and next layers. This is similar to the chain rule of derivatives in calculus.

Benefits: Backpropagation lets developers know how the points of error contribute to weights and can be trained so that a network can map while simultaneously adjusting all weights. It works well in error-prone projects and can be used to train deep neural networks.

Use cases: Backpropagation can be used in image and speech recognition, to improve the accuracy of predictions in data mining and machine learning, and in projects where derivatives must be calculated quickly.

3. Convolutional Neural Network (CNN)

What it is: The convolutional neural network (CNN) is a multilayer, feed-forward neural network that uses perceptrons for supervised learning and to analyze data. It is used mainly with visual data, such as image classification.

The massive advancements in deep learning are due in part to an exciting application of CNNs in a competition held in 2012. The success of a deep convolutional architecture called AlexNet, which was the basis for the ImageNet Large Scale Visual Recognition Competition (ILSVRC), was the primary reason for significantly accelerated research in the field of deep learning over the past several years.

However, CNN's are not limited to image recognition. They have been applied directly to text analytics and can be applied to sound when it is represented visually as a spectrogram and graph data using graph convolutional networks.

How it works: CNN architecture is different from other neural networks. To better understand this distinction, consider images as data. Typically with computer vision, images are treated as two-dimensional matrices of numbers. However, in CNNs, an image is treated as a tensor or a matrix of numbers with additional dimensions. The image below helps illustrate this concept:

 skymind

source: skymind.ai

Tensors are formed by nesting arrays within arrays, with nesting potentially occurring infinitely.

Images, in particular, are treated as four-dimensional tensors. If a scalar is a zero-dimensional object, a vector is one-dimensional, a matrice or collection of vectors is two-dimensional, and a stack of such matrices (pictured as a cube) is three-dimensional. Then a four-dimensional tensor consists of multiple such three-dimensional objects where each element in the cube has a stack of feature maps attached to it.

skymind-2

source: skymind.ai

The hidden layers in CNNs contain convolutional layers, normalization layers, pooling layers, and a fully connected layer. It takes an input image, assigns significant weights and biases to various aspects of the image to enable differentiation, and applies filters with minimum pre-processing.

While the first convolution layer captures low-level features, the next layers extract higher-level features, creating a network with a sophisticated analysis of the images in the dataset.

Benefits: The CNN algorithm is efficient at recognition and highly adaptable. It’s also easy to train because there are fewer training parameters, and is scalable when coupled with backpropagation. 

Use cases: The CNN algorithm can be used with:

  • Image processing, recognition, and classification
  • Video recognition
  • Natural language-processing tasks
  • Pattern recognition
  • Recommendation engines
  • Medical image analysis

4. Recurrent Neural Network (RNN) 

What it is: The recurrent neural network (RNN) is designed to recognize a data set's sequential attribute and use patterns to predict the next likely scenario. It is a powerful approach to processing sequential data like sound, time series data, and written natural language. The stochastic gradient descent (SGD) is used to train the network along with a backpropagation algorithm.

How it works: Unlike traditional networks, where inputs and outputs are independent of each other, in an RNN the hidden layer preserves sequential information from previous steps. This means the output from an earlier step is fed as the input to a current step, using the same weights and bias repeatedly for prediction purposes. The layers are then joined to create a single recurrent layer. These feedback loops process sequential data, allowing information to persist, as in memory, and inform the final output.

If an RNN is tasked with guessing the next letter of a previous input letter, it can be trained by feeding letters of known words letter by letter, so it determines relevant patterns. RNNs are layered to process information in two directions: feed-forward (to process data from initial input to final output) and feedback loops using backpropagation (looping information back into the network). 

RNNs are different from feed-forward networks because feed-forward networks accept one input and give one output at a time. This one-to-one constraint does not exist with RNNs, which can refer to previous examples to form predictions based on their built-in memory.

Benefits: CNNs can learn the context in sequence-prediction problems, as well as process sequential and temporal data. They also can be used in a range of applications.

Use cases: CNNs are useful for: 

  • Sentiment classification
  • Image captioning
  • Speech recognition
  • Natural language processing
  • Machine translation
  • Search prediction
  • Video classification

5. Long Short-Term Memory (LSTM) 

What it is: The long short-term memory (LSTM) algorithm is a type of RNN that allows deep recurrent networks to be trained without making the gradients that update weights become unstable. Patterns can be stored in memory for more extended periods, with the ability to selectively recall or delete data.

How it works: It uses backpropagation but is trained to learn sequence data using memory blocks connected into layers instead of neurons. As the information is processed through the layers, the architecture can add, remove, or modify data as needed.

Benefits: This algorithm is best suited for classification and prediction based on time series data, offering sophisticated results for diverse problems. These enable data scientists to create deep models using large stacked networks and handle complex sequence problems in machine learning more efficiently.

Use cases: LSTM is ideal for:

  • Captioning of images and videos
  • Language translation and modeling
  • Sentiment analysis
  • Stock market predictions

6. Generative Adversarial Network (GAN) 

What it is: The Generative Adversarial Network (GAN) is a robust algorithm used for unsupervised learning. Given a training set, the network automatically discovers and learns regularities and patterns in input data so it can self-learn to generate new data. It can essentially mimic any data set with small variations.

GANs are deep neural net architectures comprised of two nets, pitting one against the other, thus the term adversarial.

How it works: The GAN uses two submodels: generator and discriminator. The generator creates new examples of data, while the discriminator distinguishes between real domain data and fake generated samples. They run repeatedly, making them more and more robust with each repetition.

Generative and discriminative algorithms differ in a few fundamental ways:

  • Discriminative algorithms try to separate a data set into distinct classes based on similarities in their features, like classifying emails into spam and not spam. In terms of conditional probability, you could say the likelihood of a data point being class Yi given features Xi - p(y|x).
  • Generative algorithms try to determine the likelihood of a set of features in a data point that is already classified. For example, an email classified as not spam would be analyzed by the generative algorithm to find out how likely the actual words present in the email are to be present in a non-spam-type message. In terms of conditional probability, the probability of features xi existing for a data point already classified as Yi - p(x|y)0.

Benefits: GANS can capture and copy variations within a given data set, generate images from a given data set of images, create high-quality data, and manipulate data.

Use cases: GANs are useful for:

  • Cyber security
  • Health diagnostics
  • Natural language processing
  • Speech processing

7. Restricted Boltzmann Machine (RBM)

Feature mapping of the RBM Model

What it is: The Restricted Boltzmann Machine (RBM) is a probabilistic graphical model or a type of stochastic neural network. It is a robust architecture for collaborative filtering and performs a binary factor analysis with restricted communication between layers for efficient learning.

It is worth noting that RBMs have more or less been replaced by GANs or variational autoencoders by most machine learning practitioners.

How it works: The network has one layer of visible units, one layer of hidden units, and a bias unit connected to all visible and hidden units. Hidden units are independent as a way to give unbiased samples. The neurons in the bipartite graph have a symmetric connection. However, there are no connections between the nodes within a group. 

Benefits: RBM offers the advantages of energy-based learning like design flexibility, is useful for both probabilistic and non-probabilistic statistical models, restricts connectivity for easy learning, and is used with classification, regression, and generative models.

Use cases: RBM is useful for:

  • Recommender systems
  • Filtering
  • Feature learning 
  • Dimensionality reduction
  • Topic modeling

8. Deep Belief Network (DBN)

What it is: A Deep Belief Network (DBN) is an unsupervised probabilistic deep-learning algorithm where the network has a generative learning model. It is a mix of directed and undirected graphical networks, with the top layer an undirected RBM and the lower layers directed downward. This enables a pre-training stage and a feed-forward network for the fine-tuning stage.

How it works: The DBN has multiple layers of hidden units, which are connected, and the learning algorithm is “greedy” from the stacked RBMs, meaning there is one layer at a time, sequentially from the bottom observed layer. 

Benefits: DBNs offer energy-based learning and can benefit from unlabeled data.

 Use cases: DBNs are useful for:

  • Image and face recognition
  • Video-sequence recognition
  • Motion-capture data
  • Classifying high-resolution satellite image data

Why is Deep Learning Important?

Smartphones and chips are the essence of a connected network. The relevance of images, videos, and audio in social media, streaming analytics, and web searches has created a new ecosystem where these features are being monetized. The computation of such complex features requires knowledge of deep learning networks, as well as the ability to develop complex hierarchies of concepts using sophisticated algorithms. Excellent working knowledge of deep learning techniques, types of deep learning, and deep learning applications can help users execute it for various purposes. In the case of unsupervised data, machine learning may not always be feasible because manual labeling of data is expensive and time-consuming. Deep learning networks are designed to help overcome these issues.

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