Artificial Neural Networks and their applications in Computer Vision, NLP and Robotics

1.0???Preliminaries

Artificial Neural Networks (ANNs) are computer systems that can learn to perform tasks by analyzing large amounts of data, much like the human brain. They are a powerful type of machine learning algorithm used in a wide range of applications across many industries.

ANNs are used to automate complex decision-making processes, such as analyzing customer data to make better decisions about marketing, sales, and product development. They are also used in industries such as finance, healthcare, manufacturing, and retail to solve a variety of problems, from predicting equipment failures to detecting fraudulent transactions.

One of the most important aspects of ANNs is their algorithmic capabilities. They are able to learn from large amounts of data and recognize complex patterns, making them powerful tools for a variety of applications. They use methods such as backpropagation to train the network and gradient descent to optimize performance. ANNs can also be composed of multiple layers, each of which performs a different type of computation, allowing them to learn and recognize increasingly complex patterns in the data.

In today's data-driven world, ANNs are becoming increasingly important for businesses and industries looking to automate decision-making processes and gain insights from large amounts of data. As we continue to generate more and more data, ANNs will become even more critical for analyzing and understanding complex patterns in the data.

2.0???How it works

Artificial Neural Networks (ANNs) are a class of machine learning algorithms that are modelled after the structure and function of the human brain. ANNs are used for a wide range of applications, including image recognition, speech recognition, and natural language processing.

In simple terms, ANNs consist of interconnected layers of neurons that process information and make predictions. Each neuron in the network receives input from other neurons, applies a non-linear transformation to the input, and passes the output to other neurons in the network.

Now let's dive into a more detailed explanation of how ANNs work:

Phase 1: Data Preparation

The first phase in building an ANN is data preparation. This involves collecting and cleaning the data, separating it into training and testing sets, and normalizing or standardizing the data to ensure that it falls within a certain range.

Phase 2: Model Architecture

The second phase is defining the model architecture. This involves choosing the number of layers and neurons in each layer, as well as the activation function for each neuron. The architecture of the ANN is often based on trial and error and experience.

Phase 3: Forward Propagation

The third phase is forward propagation, where the inputs are fed into the network, and the output is computed using the weights and biases of the neurons. The activation function is applied to each neuron to determine its output.

Phase 4: Loss Function and Backpropagation

The fourth phase is to determine how well the model is performing by defining a loss function, which measures the difference between the predicted output and the actual output. The backpropagation algorithm is then used to update the weights and biases of the neurons based on the loss function.

Phase 5: Optimization

The fifth phase is optimization. This involves choosing an optimization algorithm, such as stochastic gradient descent, to minimize the loss function and improve the accuracy of the model. This phase is repeated until the model achieves a satisfactory level of accuracy.

Phase 6: Model Evaluation

The sixth and final phase is model evaluation. This involves testing the model on the testing data to measure its performance, such as accuracy, precision, recall, and F1 score. The performance of the model can be improved by tweaking the model architecture, adjusting the hyperparameters, and optimizing the training process.

3.0???Most Commonly Used Algorithms

Artificial Neural Networks (ANNs) are a type of machine learning algorithm that are inspired by the structure and function of the human brain. The most commonly used algorithms related to ANNs are:

·??????Multilayer Perceptron (MLP): This is a feedforward neural network that includes multiple layers of neurons and can be used for both regression and classification problems.

·??????Convolutional Neural Network (CNN): This is a type of neural network that is particularly well-suited for image classification and recognition tasks.

·??????Recurrent Neural Network (RNN): This is a type of neural network that includes loops to allow for the modeling of sequential data, such as natural language processing tasks.

·??????Long Short-Term Memory (LSTM): This is a type of RNN that includes memory cells to allow for the modeling of long-term dependencies in sequential data.

·??????Deep Belief Network (DBN): This is a type of neural network that includes multiple layers of restricted Boltzmann machines (RBMs) and can be used for unsupervised learning tasks.

·??????Autoencoder: This is a type of neural network that is used for unsupervised learning and dimensionality reduction tasks.

These are some of the most commonly used algorithms in ANNs. The choice of algorithm depends on the specific requirements of the problem, the characteristics of the dataset, and the type of neural network architecture that is best suited to the task at hand.

4.0???Application across Industries

Artificial Neural Networks (ANNs) have been widely adopted across various industries due to their ability to learn from data and make predictions with high accuracy. Here are the top 10 industry use cases for ANNs:

4.1??????Finance

ANNs are used for credit scoring, fraud detection, and stock price prediction.

Here is an explanation of how ANNs are used in finance for credit scoring, fraud detection, and stock price prediction:

Credit Scoring

ANNs are used to predict the likelihood of a borrower defaulting on a loan. The algorithm works by analyzing historical data such as payment history, credit utilization, and credit history to identify patterns that indicate a borrower's creditworthiness. The ANN is trained on a large dataset of past loan applications and their outcomes, with the goal of predicting the likelihood of default for a new loan application.

The input layer of the ANN receives data about the borrower, including their credit score, income, and employment history. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a probability score indicating the likelihood of default.

Fraud Detection

ANNs are used to identify fraudulent transactions by analyzing patterns in large datasets of transaction data. The algorithm works by identifying anomalous patterns in transaction data that are not consistent with typical behaviour.

The input layer of the ANN receives data about the transaction, including the transaction amount, time, location, and other relevant information. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a probability score indicating the likelihood of fraud.

Stock Price Prediction

ANNs are used to predict stock prices by analyzing patterns in historical stock price data. The algorithm works by identifying patterns in the data and using those patterns to predict future stock prices.

The input layer of the ANN receives historical stock price data, including prices, volume, and other relevant information. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of the future stock price.

4.2??????Healthcare

ANNs are used for disease diagnosis, patient outcome prediction, and drug discovery.

Here's an explanation of how ANNs are used in healthcare for disease diagnosis, patient outcome prediction, and drug discovery:

Disease Diagnosis

ANNs are used to assist in disease diagnosis by analyzing patient data and identifying patterns that indicate the presence of a particular disease. The algorithm works by processing patient data, such as medical records, lab results, and imaging scans, to identify patterns that are associated with specific diseases.

The input layer of the ANN receives data about the patient, including their medical history, symptoms, and test results. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a probability score indicating the likelihood of a particular disease.

Patient Outcome Prediction

ANNs are used to predict patient outcomes, such as the likelihood of complications or mortality, based on patient data. The algorithm works by analyzing large datasets of patient data to identify patterns that are associated with specific outcomes.

The input layer of the ANN receives data about the patient, including their medical history, current condition, and other relevant information. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a probability score indicating the likelihood of a particular outcome.

Drug Discovery

ANNs are used in drug discovery to analyze large datasets of chemical compounds and predict their potential effectiveness as drugs. The algorithm works by analyzing the molecular structure of compounds and identifying patterns that are associated with specific properties, such as their ability to interact with specific biological targets.

The input layer of the ANN receives data about the molecular structure of compounds, including their chemical properties and structural features. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of the compound's potential effectiveness as a drug.

4.3??????Marketing

ANNs are used for customer segmentation, churn prediction, and recommendation systems.

Here's a detailed explanation of how ANNs are used in marketing for customer segmentation, churn prediction, and recommendation systems:

Customer Segmentation

ANNs are used to group customers into segments based on their characteristics, behaviour, and purchasing history. The algorithm works by analyzing customer data, such as demographics, past purchases, and online behaviour, to identify patterns that indicate different types of customers.

The input layer of the ANN receives data about the customer, including their demographics, past purchases, and online behaviour. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a classification of the customer into one or more segments.

Churn Prediction

ANNs are used to predict which customers are likely to churn or stop using a product or service. The algorithm works by analyzing customer data, such as past purchase behaviour, customer service interactions, and demographic information, to identify patterns that are associated with churn.

The input layer of the ANN receives data about the customer, including their past purchase behaviour, customer service interactions, and demographic information. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a probability score indicating the likelihood of churn.

Recommendation Systems

ANNs are used to recommend products or services to customers based on their past behavior and preferences. The algorithm works by analyzing customer data, such as past purchases and product reviews, to identify patterns that are associated with customer preferences.

The input layer of the ANN receives data about the customer, including their past purchases and product reviews. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a set of recommended products or services based on the customer's past behavior and preferences.

4.4??????Manufacturing

ANNs are used for predictive maintenance, quality control, and defect detection.

Here's a detailed explanation of how ANNs are used in manufacturing for predictive maintenance, quality control, and defect detection:

Predictive Maintenance

ANNs are used to predict when equipment is likely to fail or require maintenance. The algorithm works by analyzing data from sensors and other sources to identify patterns that are associated with equipment failure.

The input layer of the ANN receives data from sensors and other sources, such as equipment temperature, vibration, and noise levels. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of when the equipment is likely to fail or require maintenance.

Quality Control

ANNs are used to identify defects in products or manufacturing processes. The algorithm works by analyzing data from sensors and other sources to identify patterns that are associated with defects.

The input layer of the ANN receives data from sensors and other sources, such as product measurements, images, and machine settings. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a classification of whether the product or process is defective or not.

Defect Detection

ANNs are used to detect defects in products during the manufacturing process. The algorithm works by analyzing data from sensors and other sources to identify patterns that are associated with defects.

The input layer of the ANN receives data from sensors and other sources, such as product measurements and images. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a classification of whether the product is defective or not.

4.5??????Energy

ANNs are used for load forecasting, energy demand prediction, and fault detection.

Here's a detailed explanation of how ANNs are used in the energy industry for load forecasting, energy demand prediction, and fault detection:

Load Forecasting

ANNs are used to forecast electricity demand, which is important for utilities to plan for the generation, transmission, and distribution of electricity. The algorithm works by analyzing historical data on electricity consumption and other factors that influence demand, such as weather patterns and economic conditions.

The input layer of the ANN receives data from various sources, such as weather forecasts, historical energy consumption, and economic data. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a forecast of electricity demand for a given time period.

Energy Demand Prediction

ANNs are used to predict energy demand for different types of energy sources, such as solar and wind power. The algorithm works by analyzing data on weather patterns, energy production, and other factors that influence energy demand.

The input layer of the ANN receives data from various sources, such as weather forecasts, energy production data, and historical energy demand. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of energy demand for a given time period and energy source.

Fault Detection

ANNs are used to detect faults or abnormalities in power generation, transmission, and distribution systems. The algorithm works by analyzing data on system performance and comparing it to expected performance patterns.

The input layer of the ANN receives data from various sources, such as sensor data from power generation, transmission, and distribution systems. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a classification of whether the system is operating normally or experiencing a fault or abnormality.

4.6??????Transportation

ANNs are used for route optimization, traffic prediction, and autonomous driving.

Here's a detailed explanation of how ANNs are used in the transportation industry for route optimization, traffic prediction, and autonomous driving:

Route Optimization

ANNs are used to optimize transportation routes, such as for delivery trucks, buses, or taxis. The algorithm works by analyzing data on traffic patterns, road conditions, and other factors that influence travel time and cost.

The input layer of the ANN receives data from various sources, such as traffic flow data, road condition data, and historical travel times. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces an optimized route based on the input data.

Traffic Prediction

ANNs are used to predict traffic conditions, such as congestion or accidents. The algorithm works by analyzing data on traffic flow, weather patterns, and other factors that influence traffic conditions.

The input layer of the ANN receives data from various sources, such as traffic flow data, weather forecasts, and historical traffic data. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of traffic conditions for a given time period and location.

Autonomous Driving

ANNs are used to enable self-driving vehicles to perceive their environment, make decisions, and control their movements. The algorithm works by analyzing data from various sensors, such as cameras, lidar, and radar, to detect objects and identify road conditions.

The input layer of the ANN receives data from various sensors, such as cameras, lidar, and radar, that capture information about the vehicle's environment. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a control signal that determines the vehicle's speed, direction, and other parameters.

4.7??????Retail

ANNs are used for demand forecasting, inventory optimization, and personalized recommendations.

here's an explanation of how ANNs are used in the retail industry for demand forecasting, inventory optimization, and personalized recommendations:

Demand Forecasting

ANNs are used to predict future demand for products based on historical sales data, seasonality, and other factors. The algorithm works by analyzing data on past sales and other relevant factors to identify patterns and make predictions about future demand.

The input layer of the ANN receives data on past sales, product information, and other relevant factors. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of future demand for a given product or product category.

Inventory Optimization

ANNs are used to optimize inventory levels by predicting future demand and adjusting stock levels accordingly. The algorithm works by analyzing data on sales, product availability, and other factors to determine optimal stock levels.

The input layer of the ANN receives data on sales, product availability, and other relevant factors. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a recommendation for optimal stock levels based on the input data.

Personalized Recommendations

ANNs are used to provide personalized product recommendations to customers based on their past behavior and preferences. The algorithm works by analyzing data on past purchases, browsing behavior, and other factors to identify patterns and make recommendations.

The input layer of the ANN receives data on past purchases, browsing behavior, and other relevant factors. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's recommendations. The output layer produces personalized product recommendations based on the input data.

4.8??????Agriculture

ANNs are used for yield prediction, crop classification, and disease detection.

Here's an explanation of how ANNs are used in the agriculture industry for yield prediction, crop classification, and disease detection:

Yield Prediction

ANNs are used to predict crop yields based on environmental conditions, soil quality, and other factors. The algorithm works by analyzing data on past yields, weather patterns, and other relevant factors to identify patterns and make predictions about future yields.

The input layer of the ANN receives data on past yields, weather patterns, soil quality, and other relevant factors. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of the expected crop yield.

Crop Classification

ANNs are used to classify crops based on their appearance, growth patterns, and other characteristics. The algorithm works by analyzing data on the physical characteristics of crops and using this information to classify them into different categories.

The input layer of the ANN receives data on the physical characteristics of crops, such as their color, shape, and size. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's classifications. The output layer produces a classification of the crop based on its physical characteristics.

Disease Detection

ANNs are used to detect plant diseases based on their symptoms and other factors. The algorithm works by analyzing data on the appearance of plants, environmental conditions, and other relevant factors to identify patterns and make predictions about the presence of diseases.

The input layer of the ANN receives data on the appearance of plants, environmental conditions, and other relevant factors. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of whether or not the plant is infected with a disease.

4.9??????Gaming

ANNs are used for game AI, player behavior prediction, and level generation.

Here's an explanation of how ANNs are used in the gaming industry for game AI, player behavior prediction, and level generation:

Game AI

ANNs are used in game development to create intelligent, responsive non-player characters (NPCs) that can interact with the player in realistic ways. The algorithm works by training the ANN on large datasets of gameplay scenarios and actions, and using the resulting network to control the behavior of NPCs in the game.

The input layer of the ANN receives data on the current game state, such as the player's position, health, and actions. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the network's ability to make intelligent decisions about NPC behavior. The output layer produces a decision about what action the NPC should take next.

Player Behavior Prediction

ANNs are used to predict player behavior and preferences based on their past interactions with a game. The algorithm works by analyzing large datasets of player behavior and using this information to make predictions about what actions the player is likely to take next.

The input layer of the ANN receives data on the player's past interactions with the game, such as their previous actions, game progress, and playtime. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's predictions. The output layer produces a prediction of what actions the player is likely to take next.

Level Generation

ANNs are used to generate new game levels based on pre-existing content and player preferences. The algorithm works by analyzing large datasets of game levels and using this information to create new levels that are similar to but distinct from the original content.

The input layer of the ANN receives data on the pre-existing game content and player preferences, such as the types of enemies, terrain, and obstacles that players enjoy. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the accuracy of the network's level generation process. The output layer produces a newly generated game level that meets the desired criteria.

4.10???Robotics

ANNs are used for object recognition, motion planning, and autonomous navigation.

Here's an explanation of how ANNs are used in the robotics industry for object recognition, motion planning, and autonomous navigation:

Object recognition

ANNs are used to identify and classify objects in real-time, which is a critical component of robotic perception. The algorithm works by training the ANN on large datasets of images, and using the resulting network to classify objects based on their visual features.

The input layer of the ANN receives data in the form of pixel values from a camera or other sensor. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the network's ability to recognize different objects. The output layer produces a classification of the object, such as a car, person, or tree.

Motion planning

ANNs are used to plan the optimal path for a robot to follow in a complex environment. The algorithm works by training the ANN on large datasets of possible paths and obstacles, and using the resulting network to determine the safest and most efficient path for the robot to follow.

The input layer of the ANN receives data about the environment, such as the location of obstacles and the robot's current position. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the network's ability to plan safe and efficient paths. The output layer produces a series of commands that the robot can follow to navigate the environment.

Autonomous navigation

ANNs are used to enable robots to navigate autonomously in complex environments, without human intervention. The algorithm works by combining object recognition and motion planning to enable the robot to perceive its environment, plan its path, and avoid obstacles in real-time.

The input layer of the ANN receives data from sensors and cameras about the environment, such as the location of objects and the robot's current position. This data is then processed by one or more hidden layers, with each layer applying a set of weights to the input data. The weights are adjusted during the training process to optimize the network's ability to perceive the environment and plan safe and efficient paths. The output layer produces a series of commands that the robot can follow to navigate autonomously.

5.0???Future Directions

Artificial Neural Networks (ANN) have come a long way since their inception in the 1940s. As a powerful tool for machine learning, ANN has seen a rapid growth in application areas such as image and speech recognition, natural language processing, and even autonomous driving. With the increasing demand for intelligent systems and the advancement of technology, here are some future directions for Artificial Neural Networks:

·??????Explainable AI: ANN is often regarded as a "black box" because it can be challenging to explain how the network arrives at its decision. Explainable AI aims to address this issue by providing interpretable and transparent models. Future research in ANN will focus on making the network more explainable, which can increase trust and understanding of the AI system's decisions.

·??????Reinforcement Learning: Reinforcement learning is a subset of machine learning that involves training an agent to make decisions in an environment by maximizing a reward signal. ANN has shown great potential in reinforcement learning, and future research will focus on improving the algorithm's speed and stability to enable it to handle more complex tasks.

·??????Hybrid Models: Hybrid models combine ANN with other machine learning techniques such as genetic algorithms or fuzzy logic to improve performance. In the future, researchers will investigate the integration of ANN with other algorithms to create more accurate and robust models.

·??????Edge Computing: Edge computing involves processing data near the source of the data, reducing latency and improving performance. ANN's ability to learn from large datasets makes it well-suited for edge computing. Future research will focus on optimizing ANN algorithms for edge computing applications.

·??????Neuromorphic Computing: Neuromorphic computing is an emerging field that aims to create computer systems that function similarly to the human brain. ANN has similarities to the human brain, making it an important tool for neuromorphic computing research. Future research will investigate the integration of ANN with neuromorphic hardware to create more efficient and intelligent systems.

·??????Multimodal Learning: Multimodal learning involves learning from multiple sources of data, such as images, audio, and text. ANN has shown great potential in multimodal learning, and future research will investigate the development of new architectures and techniques to improve performance.

·??????Transfer Learning: Transfer learning involves training a model on one task and transferring the learned knowledge to a new task. ANN has shown promise in transfer learning, and future research will investigate ways to improve the technique's accuracy and effectiveness.

·??????The future of Artificial Neural Networks looks bright, with ongoing research and development leading to the creation of more advanced and intelligent systems. With new applications emerging and the potential for breakthroughs in the field, ANN will continue to be a critical tool for machine learning and AI in the years to come.

Annexure I. Key Terminologies

·??????Artificial neuron: An artificial neuron, also called a node or unit, is a computational unit that receives one or more inputs and produces an output.

·??????Activation function: An activation function is a mathematical function applied to the input of an artificial neuron that determines its output. Common activation functions include sigmoid, ReLU, and tanh.

·??????Feedforward network: A feedforward network, also known as a multilayer perceptron (MLP), is a type of artificial neural network where the neurons are arranged in layers, and the output of one layer serves as the input to the next layer.

·??????Backpropagation: Backpropagation is a supervised learning algorithm used to train feedforward neural networks by adjusting the weights of the connections between neurons to minimize the difference between the predicted and actual outputs.

·??????Gradient descent: Gradient descent is an optimization algorithm used to minimize the error between the predicted and actual outputs of a neural network by adjusting the weights of the connections between neurons.

·??????Overfitting: Overfitting is a phenomenon that occurs when a model is trained too well on the training data and becomes too specialized to that data, leading to poor performance on new data.

·??????Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that encourages smaller weights in the neural network.

·??????Convolutional neural network (CNN): A convolutional neural network is a type of neural network that is particularly well-suited for image and video recognition tasks. It uses convolutional layers to automatically learn features from the input data.

·??????Recurrent neural network (RNN): A recurrent neural network is a type of neural network that is particularly well-suited for sequential data, such as time series or natural language data. It uses recurrent connections between neurons to capture temporal dependencies in the data.

·??????Long short-term memory (LSTM): A long short-term memory is a type of recurrent neural network that is designed to capture long-term dependencies in sequential data by using gated recurrent units to selectively remember or forget previous inputs.