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DL Models and Algorithms

Johns Hopkins University_012924A
[Johns Hopkins University]

- Overview

Deep learning (DL) is a branch of machine learning (ML) that uses neural networks to teach computers to do what comes naturally to humans: learn from example. 

In DL, a model learns to perform classification or regression tasks directly from data such as images, text, or sound. DL models can achieve state-of-the-art accuracy, often exceeding human-level performance. 

DL models are the structure of a DL model, while DL algorithms use neural networks to solve problems. DL models can accumulate knowledge over time, while DL algorithms can continuously analyze data and draw conclusions similar to humans.

The structure of a DL model is called its architecture, which includes the arrangement and connectivity of its layers, activation functions, and connections between neurons. 

DL models are characterized by a large number of parameters and a layered structure. Many identical neurons are organized into layers stacked on top of each other. Each neuron is described through a small number of weights and an activation function. While the activation function is typically chosen by the model’s creator (and is thus a so-called hyperparameter), the weights are learned during training. 

DL algorithms use neural networks, which are also called artificial neural networks (ANNs) or simulated neural networks (SNNs). Neural networks are a subset of machine learning and mimic the behavior of the human brain to solve complex data-driven problems. Input data is processed through different layers of artificial neurons stacked together to produce the desired output. 


- Deep Learning and Artificial Neural Networks

DL uses artificial neural networks (ANNs) to perform complex calculations on large amounts of data. It is a ML based on the structure and function of the human brain. DL algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment, and advertising commonly use DL.

The structure of a neural network is similar to that of the human brain, consisting of artificial neurons, also known as nodes. These nodes are stacked next to each other in three layers: input layer, hidden layer, output layer.

Data provides information for each node in the form of input. This node multiplies the input with random weights, computes them, and adds a bias. Finally, a non-linear function, also known as an activation function, is used to determine which neurons to fire.


- Deep Learning Algorithms

While deep learning algorithms have self-learning representations, they rely on artificial neural networks that mirror the way the brain computes information. During training, the algorithm uses unknown elements in the input distribution to extract features, group objects, and discover useful data patterns. Like training a machine to teach itself, this happens at multiple levels, using algorithms to build models. 

Deep learning models utilize several algorithms. While no network is considered perfect, some algorithms are better suited to perform specific tasks. To choose the right algorithm, it is best to have a solid understanding of all major algorithms.


- Deep Learning Models

Deep learning models are trained by using large amounts of labeled data and can often learn features directly from the data without manually extracting features. While the first artificial neural network was theorized in 1958, deep learning requires significant computing power that was not available until the 2000s. Researchers now have access to computing resources that allow them to build and train networks with hundreds of connections and neurons.

High-performance GPUs feature parallel architecture for efficient deep learning. When combined with cluster or cloud computing, development teams can reduce training time for deep learning networks from weeks to hours or less.

Deep learning models are a type of AI that use neural networks to process large datasets and create ML models. Some DL models include: 

  • Neural networks: Also known as artificial neural networks (ANN), these algorithms are based on the human brain
  • Convolutional neural networks (CNNs): These models use a grid pattern to learn feature hierarchies from low to high-level patterns, inspired by the organization of the animal visual cortex
  • Recurrent neural networks (RNNs): These models can capture spatial and temporal features from time-series and sequential data
  • PyTorch: This popular machine learning framework can run on mobile devices, allowing developers to create models that can be used without a server connection
  • TensorFlow: This popular Deep Learning library requires less computation power to produce accurate results in a given timeframe
  • Transfer learning: This technique involves developing a model for one task and then reusing it as a starting point for another task
  • Deep reinforcement learning: This model uses sequences of actions called episodes to improve over time, and compares results from different episodes
  • Naive Bayes: This algorithm learns the probability of every object, its features, and which groups they belong to, and is mainly used to solve classification problems
  • Support vector machines (SVMs): This category of deep learning methods is used for selection, correlation, and identification of outlier


- Most Popular Deep Learning Algorithms

Here is a list of the 10 most popular deep learning algorithms:

  • Convolutional Neural Network (CNN)
  • Long Short Term Memory (LSTM)
  • Recurrent Neural Network (RNN)
  • Generative Adversarial Networks (GANs)
  • Radial Basis Function Network (RBFN)
  • Multilayer Perceptron (MLP)
  • Self-Organizing Map (SOM)
  • Deep Belief Network (DBN)
  • Restricted Boltzmann Machine (RBM)
  • Autoencoder


Deep learning algorithms can process almost any type of data and require massive amounts of computing power and information to solve complex problems. 


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