Top 12 Deep Learning Algorithms You Must Know

Deep learning helps in automating tasks that were previously believed to require human intelligence. From healthcare to entertainment, its applications have become indispensable in solving real-world problems. This has greatly increased the use of automation in decision-making by imitating the behaviour of the brain.

Such automation leads to the invention of tools like product recommendations, language translations and autonomous navigation. Its ability to analyse vast datasets and uncover patterns has transformed traditional workflows into smarter, more efficient systems. Organisations are now looking into deep learning in order to progress in the ever-changing digital field.

In this blog, we will highlight the 12 important algorithms, and discuss their functions, uses and advantages in Deep Learning alongside them.

What Is Deep Learning?

Deep learning is a branch of artificial intelligence that focuses on training machines to learn and make decisions independently. It is based on the use of neural networks that can process large amounts of data, detect trends, and generate forecasts without any given rules. This technique has made a revolutionary impact on the way machines operate and comprehend various information.

Deep learning is one of the techniques that is extremely useful in dealing with unstructured data like audio, pictures, videos, and verbal communication. It is composed of layers which make it capable of perceiving large amounts of details and thus it is suitable for tasks such as speech and voice recognition, image and video analysis, and robotics. These aspects have changed so many industries for the better.

By mimicking the human brain, deep learning adapts and improves over time, offering advanced solutions for real-world challenges across domains.

Top 12 Deep Learning Algorithms

There are numerous advanced and difficult problems that AI systems can solve by using their core component which is deep learning algorithms. Here, we will cover a total of 12 core algorithms and their necessary applications as well as how they work.

Artificial Neural Networks (ANNs)

ANNs are the foundation of deep learning that replicates the functions of a human brain by using interconnected nodes, or neurons, as information processing units to receive input signals, analyse them, recognise patterns, and output predictions. Common applications of ANNs include image recognition, predictive analytics, and speech processing.

ANNs are a strong global optimiser that changes their structure and weights to solve different problems through learning. They work by improving their predictions over time through repetitive refinements and therefore have been applied in a number of areas including finance, health care, and marketing. Their ease of implementation and versatility is one of the reasons they are used as first models by many deep learning specialists.

How Do ANNs Work?

ANNs process data through layers of interconnected nodes. Here’s a simplified workflow:

• Input layer: Accepts raw data, such as numbers or pixels.
• Hidden layers: Process input data through mathematical computations to extract patterns.
• Output layer: Produces predictions or classifications based on the processed data.
• Training with backpropagation: Errors are sent backwards to adjust weights, improving the model’s accuracy.

Advantages of ANNs

• Handle both structured and unstructured data.
• Can adapt to various tasks with appropriate configurations.
• Continuously improve with more data and training.

Applications of ANNs

• Fraud detection in banking systems.
• Personalised recommendations in e-commerce platforms.
• Predictive maintenance in manufacturing.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are specialised types of neural networks that can analyse grid data, these include pictures and videos. They operate automatically to learn spatial hierarchies of features, which is particularly important for functions such as object detection, facial recognition, and image classification. With their advanced design, CNNs are able to learn the relevant information in a particular image as a result they outperform traditional algorithms in visual tasks.

A CNN consists of multiple layers that work together to extract features. Unlike regular neural networks, CNNs use convolutional layers, which scan data in smaller sections, preserving spatial relationships. This makes them highly efficient for handling large image datasets.

How Do CNNs Work?

• Convolutional layer: Extracts features like edges, corners, and textures by sliding filters over input data.
• Pooling layer: Reduces the size of the data to make computations faster and minimise overfitting.
• Fully connected layer: Combines extracted features to make predictions or classifications.
• Backpropagation: Updates weights in the network to improve accuracy during training.

Advantages of CNNs

• Excellent at image and video data analysis.
• Reduces the need for manual feature extraction.
• Scalable for large and complex datasets.

Applications of CNNs

• Medical imaging for detecting diseases like cancer.
• Real-time object detection in autonomous vehicles.
• Image-based search in e-commerce platforms.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks belong to the deep learning algorithms that are used for the sequential data. RNNs have feedback connections that make it possible to remember the preceding results in the sequence. For this reason, they are well suited for time-series forecasting and modelling, speech recognition, text and others where the process is sequential.

RNNs excel at processing sequential inputs, such as stock prices, audio signals, or written text. Their ability to “remember” past data points helps in understanding context and patterns over time. However, traditional RNNs struggle with long-term dependencies, which can affect performance in lengthy sequences.

How Do RNNs Work?

• Input and hidden layers: Process sequential data one step at a time, updating the hidden state at each step.
• Output layer: Produces a prediction based on the current input and the hidden state.
• Backpropagation through time: Adjusts weights by considering the sequence as a whole.

Advantages of RNNs

• Effective for time-series and sequential data.
• Retains contextual information in inputs.
• Can handle variable-length sequences.

Applications of RNNs

• Language modelling for predictive text input.
• Speech-to-text conversion systems.
• Anomaly detection in time-series data.

Long Short-Term Memory Networks (LSTMs)

Long Short-Term Memory Networks are a type of RNN which is used for tasks that require long-term memory. They address the issue of classical RNNs, which do not have a memory of the older data, by including memory cells, gates and a mechanism to remember and forget information as well.

LSTMs are commonly explored for use in machine translation, video captioning and sentiment classification. They are a great advantage for sequence-based systems due to their property of remembering only pertinent information amongst findings.

How Do LSTMs Work?

• Forget gate: Decides which information to discard from the memory cell.
• Input gate: Determines what new information to store in the memory.
• Output gate: Produces the final output while updating the hidden state.
• Memory cell: Retains information across time steps.

Advantages of LSTMs

• Handles long-term dependencies effectively.
• Mitigates issues like vanishing gradients.
• Flexible for various sequential data types.

Applications of LSTMs

• Machine translation for languages with complex grammar.
• Generating captions for images or videos.
• Predictive analytics in finance.

Generative Adversarial Networks (GANs)

GANs are quite different from standard deep learning since they comprise a generator and a discriminator i.e. 2 neural networks which work against each other. These neural networks are antagonistic, whereby one produces data and the other checks the data. This procedure assists GANs in creating very accurate imitation data.

GANs can be utilised for developing images, video and music. Their realistic imaging capabilities open doors to various applications from gaming to art to medical imaging and more. On the downside, training of GANs is very strenuous in computation and requires careful tuning.

How Do GANs Work?

• Generator: Produces synthetic data from random noise.
• Discriminator: Distinguishes between real and generated data.
• Adversarial training: The generator improves its output to “fool” the discriminator, which also becomes better at spotting fake data.

Advantages of GANs

• Capable of generating high-quality synthetic data.
• Useful for tasks like data augmentation.
• Can simulate real-world environments for testing purposes.

Applications of GANs

• Creating realistic images for virtual environments.
• Generating synthetic datasets for training AI models.
• Enhancing low-resolution images in photography.

Autoencoders

Autoencoders are artificial neural networks that are most commonly used in unsupervised learning tasks. It is mainly used to compress and then reconstruct the original data by processing it in a different space. In this way, important factors or patterns that might exist in the data set can be obtained, therefore, autoencoders are quite useful for dimensionality reduction or anomaly detection for instance.

These networks are mainly divided into two parts: the encoder and the decoder. The encoder takes in data and transforms it into a smaller version of itself. The decoder takes in the small version of the original data and tries to reproduce the original size data. Autoencoders are set up for the purpose of getting rid of noise and seeking important features.

How Do Autoencoders Work?

• Encoder: Transforms input data into a compressed, low-dimensional representation.
• Latent space: Stores the compressed data for efficient processing.
• Decoder: Reconstructs the original data from the latent representation.
• Reconstruction loss: Measures the difference between input and output to optimise the network.

Advantages of Autoencoders

• Efficient for dimensionality reduction and data compression.
• Effective for noise reduction in images or audio.
• Useful for unsupervised learning tasks.

Applications of Autoencoders

• Image noise removal in photography.
• Detecting anomalies in financial transactions.
• Pretraining neural networks for feature extraction.

Transformer Networks

Transformer networks are the new-age solution in the deep learning world, especially for the NLP domain. Tasks such as translation from one language to another or summarising a long document have now been made easier by the use of transformer models that have attention mechanisms hidden in them.

Attention mechanisms allow the models to concentrate on specific portions of the input instead of always having to treat every single part of it as helpful. Examples of transformer models include GPT and BERT, which are widely embraced today.

Transformers outperform traditional models like RNNs and LSTMs in handling long sequences. Their parallel processing capability makes them faster and more efficient, especially for large datasets. This versatility extends beyond text, with applications in vision and multimodal tasks.

How Do Transformer Networks Work?

• Self-attention mechanism: Assigns weights to each input token based on its importance.
• Encoder-decoder structure: The encoder processes input data, and the decoder generates outputs.
• Positional encoding: Adds location information to input data for sequence understanding.
• Feed-forward layers: Apply transformations to extract features and make predictions.

Advantages of Transformer Networks

• Handles long dependencies effectively.
• Supports parallel processing, speeding up training.
• Scales well for large datasets and complex tasks.

Applications of Transformer Networks

• Machine translation and language modelling.
• Image classification using Vision Transformers (ViT).
• Multimodal tasks combining text, image, and video data.

Deep Belief Networks (DBNs)

Deep Belief Networks (DBNs) are a type of unsupervised learning model composed of multiple layers of Restricted Boltzmann Machines (RBMs). They are particularly effective at learning hierarchical features from data, making them useful for dimensionality reduction, pretraining, and classification tasks.

DBNs work by stacking RBMs, where each layer captures higher-level features of the data. The network is trained layer by layer, starting with the first RBM. Once all layers are trained, the network can be fine-tuned for specific tasks using supervised learning techniques.

How Do DBNs Work?

• Layer-wise training: Each RBM is trained independently to extract features.
• Fine-tuning: The entire network is optimised using backpropagation for a specific task.
• Feature extraction: Each layer captures increasingly abstract representations of the input data.

Advantages of DBNs

• Effective for unsupervised learning and feature extraction.
• Can be used for pretraining deep neural networks.
• Handles complex data with hierarchical relationships.

Applications of DBNs

• Image recognition and classification.
• Dimensionality reduction for high-dimensional data.
• Feature pretraining for deep learning models.

Restricted Boltzmann Machines (RBMs)

Restricted Boltzmann Machines (RBMs) are unsupervised learning algorithms that form the building blocks for more complex models like DBNs. They are energy-based models used for tasks like dimensionality reduction, feature extraction, and collaborative filtering. RBMs consist of visible and hidden layers connected by weights, with no connections within each layer.

RBMs work by learning the probability distribution of the input data. They are trained to reconstruct inputs while capturing the underlying patterns in the data. Despite their simplicity, RBMs are powerful for specific use cases like recommendation systems.

How Do RBMs Work?

• Visible layer: Represents input data, such as user-item interactions.
• Hidden layer: Extracts features and encodes patterns.
• Energy function: Evaluate the model’s state to adjust weights during training.
• Reconstruction: Rebuilds input data based on hidden layer outputs.

Advantages of RBMs

• Suitable for unsupervised learning tasks.
• Simple structure and training process.
• Effective for collaborative filtering.

Applications of RBMs

• Movie or product recommendation systems.
• Pretraining for deep belief networks.
• Dimensionality reduction for large datasets.

Deep Q-Networks (DQNs)

Deep Q-Networks (DQNs) leverage deep learning and reinforcement learning so that machines can learn through trial and error while performing tasks. They are especially beneficial for problems such as playing games, robotic control, and navigation, which require increased rewards.

The DQQs neural network functions to estimate the Q-value which represents the expected reward towards a certain action taken in a specific environment. By learning from past experiences and iteratively improving their strategies, DQNs achieve exceptional performance in complex decision-making tasks.

How Do DQNs Work?

• State representation: Uses input data to define the current state of the environment.
• Q-value prediction: Neural networks predict the value of each possible action in a state.
• Action selection: Chooses the best action based on the highest predicted Q-value.
• Experience replay: Stores past experiences to train the model and reduce correlations.
• Target network: Stabilises learning by maintaining a separate network for Q-value updates.

Advantages of DQNs

• Handles complex, high-dimensional environments.
• Learns optimal policies without requiring explicit programming.
• Effective for sequential decision-making tasks.

Applications of DQNs

• Mastering video games like Atari.
• Robotics for precise motion control.
• Traffic light optimisation in smart cities.

Capsule Networks (CapsNets)

Capsule Networks (CapsNets) have been proposed as a potential solution to the problems posed by traditional convolutional neural networks (CNNs) in encoding spatial hierarchical relations. CapsNets are a type of neural network which groups neurons together into capsules to represent certain attributes of an object, such as orientation, pose and more. This way the model’s understanding of how certain parts of an object are related is better.

Unlike CNNs, CapsNets preserve detailed information about an object’s structure, making them more robust to changes in position, scale, and rotation. These networks are particularly useful for tasks requiring a high level of spatial awareness, like medical imaging or 3D object recognition.

How Do CapsNets Work?

• Capsules: Groups of neurons representing specific features and their spatial relationships.
• Dynamic routing: Ensures capsules communicate only with the most relevant higher-layer capsules.
• Output vectors: Encodes both the presence and properties of features.
• Reconstruction loss: Optimises the model by focusing on accurate feature representation.

Advantages of CapsNets

• Captures spatial relationships effectively.
• Robust to changes in object orientation and scale.
• Reduces the need for massive datasets.

Applications of CapsNets

• Medical imaging for tumour detection.
• 3D object recognition in augmented reality.
• Image classification in situations with limited data.

Self-Organising Maps (SOMs)

Self-organising maps (SOMs) are one type of neural network that learns in an unsupervised fashion and generates a set of clusters containing similar data. However, SOMs, in contrast to other neural networks, transform high-dimensional vectors into low-dimensional image grids while preserving the data’s topological properties. Thus, SOMs are useful for clustering and classifying and, most importantly, for analysing complex datasets.

SOMs use competitive learning, where neurons compete to represent input data. The winning neuron adjusts itself and its neighbours to better match the input, gradually forming an organised map of the data.

How Do SOMs Work?

• Input layer: Receives high-dimensional data points.
• Competitive learning: Neurons compete to become the best match for the input.
• Neighbourhood update: Adjusts neighbouring neurons to reflect similar patterns.
• Low-dimensional mapping: Organises the data into clusters on a 2D or 3D grid.

Advantages of SOMs

• Simplifies high-dimensional data for easy visualisation.
• Effective for clustering and anomaly detection.
• Preserves topological relationships in the data.

Applications of SOMs

• Customer segmentation in marketing.
• Dimensionality reduction for exploratory data analysis.
• Fraud detection in financial systems.

How to Choose the Right Algorithm?

Choosing the right deep learning algorithm depends on various factors related to the problem and dataset. Here are some key considerations:

• Type of Data:
  • Use CNNs for image and video data.
  • Opt for RNNs or LSTMs for sequential data like text, audio, or time-series data.
• Problem Objective:
  • Select GANs for generating synthetic data or enhancing existing data.
  • Choose Autoencoders for tasks like noise reduction or anomaly detection.
• Complexity of Data:
  • If spatial hierarchies matter, like in 3D recognition, go for CapsNets.
  • For high-dimensional data, consider SOMs or DBNs for feature extraction and clustering.
• Computational Resources:
  • If resources are limited, avoid models like Transformers, which require significant memory and processing power.
  • Choose simpler models like ANNs or RBMs for basic tasks.
• Size of the Dataset:
  • Use Transformer Networks for large datasets with rich context.
  • Opt for SOMs or Autoencoders for smaller datasets.
• Real-Time Requirements:
  • For real-time applications like autonomous driving, prefer efficient models like CNNs or DQNs.
• Learning Goal:
  • If unsupervised learning is needed, use SOMs, RBMs, or Autoencoders.
  • For reinforcement learning tasks, choose DQNs or similar models.

By evaluating these factors, you can align the algorithm’s strengths with your specific requirements.

Conclusion

Deep learning algorithms are a true game-changer in the domain of artificial intelligence as they allow a machine to solve seemingly impossible problems. These are algorithms that find application in automated sectors across industries from healthcare to entertainment in the form of systems that improve the efficiency and experience of the users. Their multifaceted nature makes them priceless everywhere.

With the pace at which technology is progressing, the requirement for knowledge and application of deep learning algorithms will increase exponentially. For an AI lover or a practitioner, getting acquainted with these algorithms allows one to generate an idea and do something meaningful and practical. Get more insights on deep learning with the Integrated Program in Data Science, Artificial Intelligence, & Machine Learning in collaboration with MIT Open Learning by Hero Vired and professional certification.