Discover How Neural Networks Work to Transform Modern AI!
By upGrad
Updated on Jul 03, 2025 | 14 min read | 34.12K+ views
Share:
For working professionals
For fresh graduates
More
By upGrad
Updated on Jul 03, 2025 | 14 min read | 34.12K+ views
Share:
Table of Contents
Did you know that over 70% of Indian organizations using AI have moved beyond pilot phases, with neural networks increasingly embedded in core operations and analytics workflows. This shift highlights the need to understand how neural networks work, enabling advancements in AI, data processing, and predictive analytics. |
Neural networks work by processing data through layers of interconnected neurons, learning patterns, and making predictions based on input. These networks adapt and improve by adjusting weights and biases during training.
Understanding how neural networks work is crucial for building models that can solve complex tasks efficiently. This knowledge is fundamental to machine learning, driving innovations in areas such as computer vision, natural language processing, and predictive analytics.
In this blog, we will understand how neural networks work along with their practical applications, and more.
Want to learn the fundamentals of neural networks to build powerful AI applications? upGrad’s Artificial Intelligence & Machine Learning - AI ML Courses can equip you with tools and strategies to stay ahead. Enroll today!
Neural networks are a subset of AI, taking inspiration from the biological neural network of the human brain, consisting of interconnected nodes or "neurons." These neurons process input, pass output, and enable the network to make predictions, recognize patterns, and solve problems.
Modeled after the brain's biological networks, artificial neurons adjust their connections based on input. This learning process improves performance, allowing for more accurate results over time.
Want to explore how neural networks shape the future of AI? Check out these top-rated programs and advance your career:
Neural networks work by simulating the human brain’s structure, where interconnected neurons process input data through multiple layers to make predictions. These networks are central to deep learning models, enabling machines to handle complex, unstructured data and improve over time as they learn from each iteration.
Also read: NLP Applications in Business: Real-World Use Cases & Benefits
Let’s now explore the types of neural networks and how neural networks work to solve specific challenges.
Neural networks come in various architectures, each designed to tackle specific challenges and excel in particular tasks. Understanding their structure, function, and best-use scenarios is essential to grasping the neural network basics and learning how neural networks work.
Below, you'll explore the most common types of neural networks and how they operate.
The Feedforward Neural Network (FNN) is the least complex type of neural network. In FNN, data flows in one direction— from the input layer to the output layer—without any loops or cycles.
A Recurrent Neural Network (RNN) is designed for sequential data, such as time-series data, speech, or text, by maintaining a form of memory from previous inputs.
Convolutional Neural Networks (CNNs) are specialized for processing grid-like data, particularly images, through layers designed to detect patterns in the data.
Also Read: CNN vs RNN: Difference Between CNN and RNN
The Radial Basis Function Network (RBFN) is a type of neural network that uses radial functions to map input data into high-dimensional spaces.
Modular Neural Networks divide complex tasks into smaller sub-networks, each focused on solving a specific part of the problem.
To understand how neural networks work, let’s examine the training process and how networks refine predictions through iterative learning.
Neural networks work by training a model to recognize patterns and make accurate predictions through iterative learning. This process involves supervised and unsupervised learning techniques, with tools like Matplotlib, Numpy, and Pandas used for data handling and visualization.
Also Read: Supervised vs Unsupervised Learning: Difference Between Supervised and Unsupervised Learning
Now, let’s understand how neural networks work in practical applications, driving innovation and solving complex problems across various industries.
Neural networks are powering advanced machine learning models that solve complex problems and enhance data-driven decision-making. Understanding how neural networks work is key to their application in areas like computer vision, speech recognition, and NLP.
Enhance your skills and stay ahead in the tech world by enrolling in courses like the LL.M. in AI and Emerging Technologies. Learn about the latest advancements and boost your professional profile.
Also Read: Deep Learning Vs NLP: Difference Between Deep Learning & NLP
To optimize how neural networks work, it’s essential to address their challenges and limitations in practical applications.
While neural networks work effectively in many applications, they are not without their challenges. These obstacles, including data quality, computational demands, and interpretability, must be addressed to maximize their potential and improve overall efficiency.
Learning ‘how neural network works’—from basic neurons and layers to advanced algorithms—can help make these models more understandable. Despite the challenges, overcoming these obstacles is key to mastering neural networks.
That's where upGrad can help. They offer structured learning paths to help you tackle these complexities and build the skills needed to work with advanced AI models.
Also read: 15+ Top Natural Language Processing Techniques To Learn in 2025
Understanding how neural network works involves learning the fundamentals of weight optimization, backpropagation, and activation functions. To optimize performance, focus on fine-tuning hyperparameters such as batch size, epochs, and learning rates.
Developers often struggle with issues like overfitting and high computational demands, especially with large datasets. upGrad’s additional courses in deep learning and neural networks can equip you with the skills to tackle these challenges effectively.
Not sure where to start with neural networks and AI? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.
Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.
Discover in-demand Machine Learning skills to expand your expertise. Explore the programs below to find the perfect fit for your goals.
Discover popular AI and ML blogs and free courses to deepen your expertise. Explore the programs below to find your perfect fit.
Reference:
https://www.ey.com/content/dam/ey-unified-site/ey-com/en-in/insights/ai/documents/ey-nasscom-ai-adoption-index.pdf
523 articles published
We are an online education platform providing industry-relevant programs for professionals, designed and delivered in collaboration with world-class faculty and businesses. Merging the latest technolo...
Get Free Consultation
By submitting, I accept the T&C and
Privacy Policy
Top Resources