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Explore 8 Must-Know Types of Neural Networks in AI Today!

Explore 8 Must-Know Types of Neural Networks in AI Today!

Q: 1. What is the difference between CNNs and RNNs?

CNNs are designed for processing image data by learning spatial hierarchies of features, making them effective for tasks like image classification. On the other hand, RNNs are specialized for sequential data, where each input is dependent on the previous one. RNNs have an internal memory to process time-series or language-related data. CNNs excel in visual data, while RNNs are best suited for tasks like language processing and time-series forecasting.

Q: 2. What are the limitations of neural networks?

Neural networks require large datasets to perform effectively, and obtaining this data can be time-consuming and expensive. They are also computationally expensive, requiring significant processing power, especially for deep learning models. Neural networks are prone to overfitting, which can occur if the model is too complex for the available data. Additionally, they can lack transparency, making it difficult to interpret their decision-making process.

Q: 3. What is transfer learning in neural networks?

Transfer learning involves taking a pre-trained neural network, typically trained on a large dataset, and adapting it to a new but related task. This approach allows you to use the knowledge already learned by the model, reducing the amount of data and time required for training. It is particularly useful when dealing with limited data for the target task. By fine-tuning the pre-trained model, you can save computational resources and improve model performance.

Q: 4. Can neural networks be used for regression tasks?

Yes, neural networks are well-suited for regression tasks, where the goal is to predict continuous values. In regression problems, the output layer of the neural network is adjusted to provide a continuous output, rather than classifying data into discrete categories. Feedforward neural networks (FNNs) are commonly used for regression tasks, where the network learns to predict numerical values. This makes them effective for applications like stock price prediction or demand forecasting.

Q: 5. What is the role of activation functions in neural networks?

Activation functions introduce non-linearity into neural networks, enabling them to learn complex patterns in data. Without activation functions, the network would essentially be a linear regression model, limiting its ability to solve more intricate problems. Common activation functions like ReLU, sigmoid, and tanh determine whether a neuron should be activated based on the input. These functions allow neural networks to model complex relationships in data, which is crucial for tasks like image recognition and natural language processing.

Q: 6. What are the advantages of LSTMs over traditional RNNs?

LSTMs are a more advanced form of RNNs, designed to address the vanishing gradient problem, which occurs in standard RNNs when learning long-term dependencies. LSTMs achieve this by using memory cells, which can store information over longer sequences, making them ideal for tasks like language translation and speech recognition. The architecture of LSTMs allows them to maintain context over extended time periods, unlike traditional RNNs. This capability is critical for tasks that require understanding the relationship between distant elements in a sequence.

Q: 7. How do Generative Adversarial Networks (GANs) work?

GANs consist of two networks: a generator and a discriminator. The generator creates synthetic data, such as images, while the discriminator evaluates whether the data is real or fake. The two networks are trained in opposition to each other, with the generator improving its output based on feedback from the discriminator. This process allows GANs to generate highly realistic data, which has applications in areas like image synthesis, data augmentation, and creative AI.

Q: 8. What is the vanishing gradient problem in neural networks?

The vanishing gradient problem occurs when the gradients used in backpropagation become very small as they propagate through the network, causing the model to stop learning effectively. This issue is most prominent in deep neural networks, particularly in RNNs, where long-term dependencies need to be captured. As a result, the model struggles to adjust weights for earlier layers in the network. LSTMs and other specialized models have been developed to mitigate this problem by maintaining more stable gradients during training.

Q: 9. What is the purpose of dropout in neural networks?

Dropout is a regularization technique used to prevent overfitting in neural networks by randomly disabling a fraction of neurons during training. This forces the network to learn more effective and generalized features, preventing the model from relying too heavily on any single neuron. Dropout is typically applied during training, and during inference, all neurons are used. This technique improves the model’s ability to generalize to new, unseen data.

Q: 10. What are Autoencoders in neural networks?

Autoencoders are a type of unsupervised learning model designed to learn efficient representations of input data by compressing it into a lower-dimensional space and then reconstructing it. They consist of an encoder, which maps input data to a compressed representation, and a decoder, which reconstructs the data from this representation. Autoencoders are often used for dimensionality reduction and anomaly detection, as they can identify unusual patterns in data. Their ability to compress data makes them valuable for tasks like image denoising and feature extraction.

By Pavan Vadapalli

Updated on Jul 08, 2025 | 12 min read | 26.9K+ views

Table of Contents

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Top 8 Types of Neural Networks Shaping AI in 2025
Choosing the Best Neural Network Type for 2025 Tasks!
Neural Networks in 2025: Key Trends and Emerging Developments
How upGrad Can Help You With Neural Networks and AI in 2025!

Did you know that 87% of Indian companies are in the middle stages of AI adoption maturity? This highlights the growing demand for professionals skilled in different types of neural networks to build scalable, AI-driven solutions.

CNNs, RNNs, and LSTMs are core neural network types powering image recognition, language models, and time-series prediction. These networks use layered architectures and weighted connections to extract features, retain context, and efficiently handle both structured and unstructured data.

In this blog, we’ll explore the eight popular types of neural networks in AI, highlighting their unique structures, functions, and practical applications.

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Top 8 Types of Neural Networks Shaping AI in 2025

Neural network models are foundational to artificial intelligence, enabling machines to process data in ways that mimic human cognition. Inspired by the biological neuron, these networks consist of interconnected nodes that process information and adjust their parameters through learning.

Enhance your expertise in neural networks, AI, and deep learning with these industry-relevant courses:

Various types of neural networks have been developed, each tailored to specific tasks and applications. Below is an overview of some common types:

1. Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are an important computer vision algorithm designed to process grid-like data, such as images. They automatically learn spatial hierarchies of features, which makes them essential for image-related tasks like feature extraction for classification or detection.

Image Recognition: CNNs identify objects in images or videos, such as in facial recognition systems.
Autonomous Vehicles: Real-time object detection helps self-driving cars identify pedestrians, vehicles, and road signs.
Medical Image Analysis: CNNs analyze medical scans like MRIs and X-rays to detect early signs of diseases.

Real-World Use Case:

Practo is using CNNs to analyze medical images, enabling doctors to make faster, more accurate diagnoses. This technology is particularly valuable if you are operating in remote areas, providing quicker access to expert care and improving overall health outcomes.

Also Read: Basic CNN Architecture: A Detailed Explanation of the 5 Layers in Convolutional Neural Networks

2. Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are designed for sequential data, where the output depends on previous inputs. This is ideal for applications that involve time-series or ordered data, such as speech and language processing.

They have a built-in memory that retains information from previous steps, allowing them to process sequences of arbitrary length. However, traditional RNNs struggle with long-term dependencies, which have been addressed by models like LSTMs and GRUs.

Natural Language Processing (NLP): RNNs are commonly used in NLP tasks such as machine translation, text generation, and speech-to-text conversion.
Time-series Prediction: RNNs can predict future values based on historical data, making them valuable for stock market predictions and weather forecasting.
Speech Recognition: Used in systems like Siri and Alexa, RNNs enable accurate conversion of speech to text by considering the context of previous words.

Real-World Use Case:

Freshworks applies RNN-based models in its customer support systems to maintain conversational context and improve multilingual query resolution. By modeling sequential input patterns, RNNs enable accurate intent detection and adaptive response generation across dynamic support environments..

Also Read: CNN vs. RNN: Key Differences and Applications Explained

3. Radial Basis Function (RBF) Networks

Radial Basis Function Networks are a type of neural architecture used for classification, regression, and function approximation. These models solve non-linear problems using distance-based activation functions, making them suitable for real-time applications across dynamic environments.

Function Approximation: Estimates unknown functions using radial basis activation, useful in industrial simulations built with Python or Scala.
Time-Series Prediction: Forecasts future trends using historical input data, often deployed in lightweight Flask-based analytics tools.
Control Systems: Enables real-time robotic control by adjusting movements based on sensory feedback and centroid proximity.

Real-World Use Case:
GreyOrange uses RBF networks to guide warehouse robots in real-time path correction and collision avoidance. These models support fast sensor processing and backend coordination using Scala services.

4. Long Short-Term Memory Networks (LSTMs)

LSTMs are advanced types of neural networks that improve over traditional RNNs by capturing long-term dependencies in sequential data. These models are ideal for applications like text, speech, and time-series analysis, especially when built with TensorFlow and deployed using Docker environments.

Handwriting Recognition: LSTMs extract sequential features from handwritten text using TensorFlow pipelines integrated into document scanning solutions.
Speech Synthesis: These models generate human-like speech patterns and are used in AI voice engines with Docker-based model deployment.
Machine Translation: LSTMs translate long-form sentences while retaining semantic context, often trained using parallel corpus data in TensorFlow.

Real-World Use Case:
Reverie Language Technologies uses LSTMs to enhance the accuracy of Indian language translations across regional apps and government platforms. The models are trained in TensorFlow and containerized with Docker for scalable deployment.

Also read: Exciting 40+ Projects on Deep Learning to Enhance Your Portfolio in 2025

5. Multilayer Perceptrons (MLPs)

Multilayer Perceptrons in machine learning are one of the foundational types of neural networks used for classification and regression on structured datasets. MLPs work best when the data is independent and identically distributed, and they can be easily trained using Apache Spark MLlib for scalable processing.

Simple Classification Tasks: MLPs classify structured tabular data using fully connected layers, making them suitable for churn prediction and fraud detection.
Regression Problems: These networks model continuous outputs such as sales forecasts or pricing trends in retail systems powered by Spark.
Basic Data Processing: MLPs efficiently handle low-dimensional structured data without the need for CNNs or RNNs, particularly in real-time dashboards.

Real-World Use Case:
Flipkart uses MLPs for demand forecasting and personalized product recommendations on low-complexity data segments. These models are trained on Apache Spark clusters for faster computation and real-time inference.

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If you want to build a solid foundation in neural networks, check out upGrad’s Fundamentals of Deep Learning and Neural Networks. The 28-hour program covers essential neural network concepts, including backpropagation, activation functions, and deep learning architectures for practical AI tasks.

6. Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of neural networks comprising two components: a generator that creates data and a discriminator that evaluates it. GANs learn from real data distributions to produce synthetic yet realistic outputs, ideal for unsupervised learning and augmentation tasks.

Image and Video Synthesis: GANs generate photorealistic visuals used in creative AI workflows, often orchestrated with Go and stored using SQL databases.
Data Augmentation: In finance and healthcare, GANs synthesize data to enrich small datasets, improving model performance in structured SQL-driven pipelines.
Unsupervised Learning: GANs uncover hidden patterns without labels, supporting anomaly detection and simulation in SQL-integrated analytics systems.

Real-World Use Case:
Wipro applies GANs to generate synthetic patient records for training healthcare analytics models under privacy-preserving conditions. These models are deployed using Go-based microservices with SQL integration for secure, queryable data storage.

If you want to deepen your understanding of neural network data handling, check out upGrad's Advanced SQL: Programming Constructs & Stored Functions. The 11-hour free program helps you write efficient SQL queries to preprocess, organize, and query structured datasets for training neural networks.

7. Deep Belief Networks (DBNs)

Deep Belief Networks are layered generative models made up of stacked Restricted Boltzmann Machines, used primarily for unsupervised learning and weight initialization. DBNs extract hierarchical representations from input data, making them useful for enhancing performance in supervised deep learning tasks.

Image and Speech Recognition: DBNs pre-train image and voice models, improving recognition accuracy in systems built with Java and deployed on web-based HTML or CSS interfaces.
Dimensionality Reduction: This technique compresses high-dimensional data into meaningful, low-dimensional features, streamlining preprocessing for classification and clustering models.
Pre-training Deep Networks: DBNs initialize weights in deep neural networks, speeding up convergence and reducing overfitting during supervised training.

Real-World Use Case:
Zoho uses DBNs to pre-train internal NLP and classification models that support email sorting and customer query triage. These models interface with Java-driven backends and web tools styled in HTML and CSS for seamless user interaction.

Also read: Top 25 Artificial Intelligence Projects in Python For Beginners

8. Self-Organizing Maps (SOMs)

Self-Organizing Maps are unsupervised neural networks that project high-dimensional data onto a two-dimensional grid while preserving its topological structure. SOMs are ideal for clustering, visualization, and anomaly detection, especially when deployed on cloud platforms like AWS and Azure for scalable processing.

Customer Segmentation: SOMs group users based on behavioral data, often integrated with AWS SageMaker for real-time targeting in marketing pipelines.
Anomaly Detection: These models detect fraudulent transactions or network intrusions by identifying outliers in Azure-hosted log datasets.
Dimensionality Reduction: SOMs visually represent complex datasets, supporting decision-making in dashboards built with AWS QuickSight or Azure ML.

Real-World Use Case:
Paytm applies SOMs to segment users for personalized offers and detect transaction anomalies in its digital payment infrastructure. These models are trained on AWS and integrated into Azure-based monitoring dashboards for continuous risk assessment.

If you want to gain expertise in cloud engineering, check out upGrad’s Master the Cloud and Lead as an Expert Cloud Engineer. The program covers cloud deployment and migration, as well as the use of neural networks within AI and ML cloud applications.

To choose the right type of neural network, consider input format, learning objective, and real-time or batch deployment needs.

Choosing the Best Neural Network Type for 2025 Tasks!

To select the best model from various types of neural networks, assess the task-specific requirements and data structure. Your choice impacts accuracy, resource usage, and deployment feasibility.

Image Data: Use CNNs for tasks like object detection, segmentation, or classification where spatial relationships matter.
Sequential Data: Opt for RNNs or LSTMs when working with time-series, language models, or speech where order and memory are critical.
Text Analysis: RNNs, LSTMs, or Transformers suit NLP tasks involving context-aware sequence modeling or long-term dependencies.
Tabular or Structured Data: Apply MLPs for classification or regression when inputs are independent and lack spatial or temporal structure.
Data Dimensionality: Use DBNs or SOMs for reducing dimensions in high-volume datasets with visualization or clustering needs.
Synthetic Data Requirements: Select GANs when generating synthetic datasets or augmenting training data under constraints of privacy or scarcity.
Training Constraints: Select simpler networks, such as MLPs, when computational power is limited or datasets are small and structured.
Real-Time Processing: Prefer optimized CNNs or shallow LSTMs for real-time use cases where latency and inference speed are critical.

Know more: Understanding What is Feedforward Neural Network: Detailed Explanation

Let’s explore how the types of neural networks are advancing in 2025 to support more adaptive, scalable, and intelligent systems.

Neural Networks in 2025: Key Trends and Emerging Developments

A whopping 80% of companies in India have identified AI as a core strategic priority, higher than the global average of 75%. With advancements in quantum computing, ethics, and edge processing, their impact is expected to expand across multiple industries.

Here are the emerging trends in neural networks that will drive technological innovation:

1. Quantum Computing and Neural Networks

Quantum computing has the potential to accelerate neural network performance by solving complex problems faster than traditional computers. By combining quantum mechanics with neural networks, computations can be processed at unprecedented speeds.

Future Impact: As quantum technologies mature, they may enable neural networks to simulate highly complex phenomena, such as predicting climate change patterns, with significantly higher accuracy and speed.
Example: Quantum-powered neural networks could expedite drug discovery by processing large-scale molecular data faster, enabling quicker breakthroughs in personalized medicine.

Quantum computing will unlock new possibilities for AI, enhancing the efficiency of neural networks in critical fields like healthcare.

2. AI Ethics and Explainability

As neural networks become more complex, understanding their decision-making process will be crucial. The future will focus on explainable AI (XAI) to address transparency and bias concerns.

Future Impact: Explainable neural networks will be pivotal in regulatory compliance, ensuring that AI-driven decisions in sensitive areas like healthcare are fully auditable and trustworthy.
Example: In finance, transparent neural networks can ensure fair lending decisions, reducing bias and increasing consumer trust in AI-powered systems.

By allowing explainability, AI systems will become more reliable and ethically sound in high-stakes industries.

3. Neural Networks in Edge Computing

Edge computing allows neural networks to process data locally on devices, reducing latency and making real-time decision-making possible. This trend will enable faster, more responsive AI applications.

Future Impact: With AI processing on edge devices, industries like manufacturing could see improved predictive maintenance, reducing downtime, and improving safety by identifying machine faults before they occur.
Example: In smart cities, edge-based CNNs can optimize traffic flow by analyzing camera feeds in real-time, reducing congestion.

Edge computing will make AI applications more efficient, especially in autonomous vehicles and IoT systems, enhancing real-time decision-making.

4. Enhanced Natural Language Understanding

Advancements in NLP models will push neural networks beyond simple text generation to understanding deep contextual meanings and emotions, revolutionizing communication tools.

Future Impact: Advanced NLP models will enable AI to better understand cultural and regional context, allowing businesses to provide more localized services and content to global audiences.
Example: Chatbots with advanced NLP models could provide personalized, emotionally intelligent customer support, improving user experience.

Also read: Scope of Artificial Intelligence in Different Industries Explained

How upGrad Can Help You With Neural Networks and AI in 2025!

CNNs, RNNs, and LSTMs each serve specific tasks, such as image processing, time-series prediction, and speech generation. To apply these types of neural networks effectively, you need structured learning and hands-on practice.

Many struggle with model selection, optimization, and real-world deployment due to lack of guided experience. upGrad offers practical training to build, deploy, and refine neural networks tailored to industry use cases.

Here are some additional courses that can help you on your learning journey:

Confused about which neural network to learn first? Talk to upGrad’s counselors or visit a nearby upGrad career center. With expert support and an industry-focused curriculum, you'll advance your career.

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References:
https://community.nasscom.in/communities/digital-transformation/ai-adoption-index-20-tracking-indias-sectoral-progress-ai
https://www.cnbctv18.com/technology/bcg-2025-ai-radar-report-indian-companies-artificial-intelligence-initiatives-19540638.htm

Frequently Asked Questions (FAQs)

1. What is the difference between CNNs and RNNs?

2. What are the limitations of neural networks?

3. What is transfer learning in neural networks?

4. Can neural networks be used for regression tasks?

5. What is the role of activation functions in neural networks?

6. What are the advantages of LSTMs over traditional RNNs?

7. How do Generative Adversarial Networks (GANs) work?

8. What is the vanishing gradient problem in neural networks?

9. What is the purpose of dropout in neural networks?

10. What are Autoencoders in neural networks?

11. How can neural networks be used in autonomous vehicles?

Pavan Vadapalli

900 articles published

Pavan Vadapalli is the Director of Engineering , bringing over 18 years of experience in software engineering, technology leadership, and startup innovation. Holding a B.Tech and an MBA from the India...

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