It has become commonplace to refer to artificial intelligence (AI) in a futuristic sense; however, AI is now ubiquitous, from facial recognition on our smartphones to the personal recommendations offered by our streaming services. There is also a growing arsenal of conversational AI that is transforming industry around the world. But one thing underpins all of this technology: the artificial neural network. So how do these machines ‘think’? How do they learn? How do they make decisions? In this guide to neural networks, we will look at the mechanics of neural networks and their various architectures and show how they are used today in a variety of contexts. Whether you are a data scientist looking for that next step, a business looking to exploit the latest technology, or just a tech enthusiast curious to find out more about how computers can learn from data, this is the guide for you.
What is Neural Network?
The title of this guide, “Neural Networks Explained," hints at the task ahead of us in this article: to explain neural networks. One of the easiest ways to explain such complex systems is by comparing them with other, very complex systems as well: our own brains. To be more precise, the human brain consists of roughly 86 billion interconnected neurons. We already mentioned above the process of learning by touching something hot. Also, recognizing a friend’s voice while talking to him is done by sending signals through neurons and through their synapses, which are the connections between the neurons.
The fundamental architecture of an artificial neural network is that of software that mimics the functionality of neurons and their interconnections within a living brain. Such networks are composed of thousands to billions of artificial neurons, or so-called nodes, which are distributed over various layers of a network.
[Input Data] ──► [Artificial Neuron / Node] ──► [Output Prediction] ▲ (Weighted Math + Activation)
▲
(Weighted Math + Activation)
Unlike traditional programming, where a developer could explicitly write a program to solve a problem, such as recognizing handwritten numbers, by writing out a series of step-by-step rules, i.e., if X then Y, the complex nature of many problems nowadays means that there are too many exceptions for a developer to write out for a computer. This is where machine learning and deep learning come in, where a neural network can be trained on a massive amount of data to learn how to recognize certain things and improve its performance over time by refining its own parameters.
Anatomy of a Neural Network: Core Components
So far in the Neural Networks Explained tutorial, we have covered the basic structure of a system which contains a number of nodes arranged in a specific pattern. We have seen that in a standard neural network, there are three main layers of function: the input layer, the hidden layers and the output layer.
1. The Input Layer
- Inputs for a neural network are fed into the input layer of a network. Here, the information that has been fed into a system in the form of physical signals or in the form of files is converted into a numerical vector so that it can be processed by the neural network.
- For image recognition, inputs represent pixel brightness values.
- For a financial forecast, for example, the input layer would accept historical stock prices, interest rates and trade volumes.
- Word embeddings of individual words in natural language processing applications are fed into the input layer of a neural network as individual inputs.
2. The Hidden Layers
- The real magic happens within the hidden layers. Situated between the input and output stages, these intermediate layers perform a series of mathematical transformations to extract features from the data.
- When a network only contains one hidden layer, it is called a shallow neural network. If a network has multiple hidden layers—sometimes hundreds deep—it is referred to as a deep neural network, and the field of such models is called deep learning. Each subsequent layer consists of increasingly abstract feature representations.
- Layer 1: Detects simple edges and color transitions.
- Layer 2: Combines edges into geometric shapes and textures.
- Layer 3: Shapes are aggregated together to form more complex patterns. These can include parts such as eyes, , etc.
- Layer 4 and above: Objects and Concepts (e.g. golden retriever, sports car)
3. The Output Layer
- The output layer or outermost layer, is the final processing unit or section of a neural network that puts out the results to all issues that are processed by a neural network such as recognition of objects in relation to images that pass by the neural network. The structure of this layer or section is typically equal to the number of options the network has been trained to recognize. A simple example of this would be that of a spam detector or filter, which would have 2 units or nodes in the output layer since it is trained to recognize spam and not spam.
- Binary Classification: A single output node that can generate a value between 0 and 1, such as Spam vs. Not Spam in email filtering.
How Neural Networks Work: Step-by-Step
Understanding how neural networks work requires looking at the mathematical mechanics under the hood. Don’t worry if math isn’t your strong suit, because we break down the process into four easy-to-follow phases: forward pass, Error Calculation, backpropagation, and parameter updating.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Forward Pass │ ───► │ Calculate Loss │ ───► │ Backpropagation │
│ (Compute Output)│ │ (Measure Error)│ │(Compute Gradient)│
└─────────────────┘ └─────────────────┘ └─────────────────┘
▲ │
└──────────────── Adjust Weights ─────────────────┘
- Phase 1: Weights, Biases, and Forward Propagation
There is a parameter on every connection between neurons, called a weight. The weight of a connection affects how much that connection affects the total input to a neuron. There is also a parameter, called a bias, that is added to the total input of a neuron. The bias affects the threshold of activation for a neuron, which allows the model to learn trends that are not linear.
In the forward pass, the data from the input of a neuron goes through the neuron’s layers one by one, starting from the input layer and moving all the way to the hidden layers. Within every neuron, there are two main steps involved: the weighted sum calculation and the activation function application.
- Weighted Sum Calculation: This is the sum of the products of each input, $x_i$, and its corresponding weight, $w_i$, plus the bias, $b$:
- $$\text{Weighted Input} = \sum (x_iw_i + b$$
- Activation Function Application: To allow the model to learn complex, non-linear relationships, the weighted sum is passed through an activation function.
- In a linear model, no matter how many layers you stack together, it is simply equal to a big linear equation, or a straight line. The non-linearity that activation functions introduce into a neural network enables it to draw the kind of decision boundaries that are required to solve very complex classification problems.
- Popular Activation Functions
- ReLU (Rectified Linear Unit): The ReLU function simply sets all negative numbers on input to zero and all the positive numbers on input to that number. This activation function is very fast and hence is used for hidden layers within a neural network.
- Sigmoid: Maps real values to real values in the range (0, 1)—It is often used for binary output classification problems.
- Softmax: This activation function can be used for outputs that are numerical in nature. Softmax makes a set of numbers into a probability distribution where the numbers are the relative likelihood of the output of the distribution taking on the corresponding value. The sum of all output values is always 100% or 1.0. This activation function is typically used in multi-class classification.
Phase 2: Loss Calculation (Measuring Error)
Output is generated in the output layer of the forward pass and the prediction of the network is compared to the ground truth in the loss function (or cost function).
The loss function also measures the difference between a model’s predictions and ground truth for each image (for classification, this might report how confident the model was that an image was a cat when it in fact was a cat — resulting in a large error for that sample). For training a neural network, the loss function is tuned to drive this to as close to zero as possible.
Phase 3: Backpropagation and Optimization
How does the network learn from mistakes? That’s right, backpropagation.
The term backpropagation is often used in the context of neural networks. This is because backpropagation is used in conjunction with the four other phases to train a network. In this context, backpropagation uses calculus (specifically the chain rule) to compute the change in each weight and the respective loss function for the changes in the individual weights and biases of the neural network. This is done by recursively applying the chain rule to compute the error gradients that flow from the output of the network back to the input of the network.
Phase 4: Gradient Descent (Updating Parameters)
Using the calculated gradients in the weight and bias parameters of the neural network, an optimization algorithm such as Gradient Descent or Adam tries to decrease the error made by the model in the learning process.
Imagine walking down a foggy mountain in search of the lowest valley: gradient descent checks the slope under your feet and takes a step downward. The size of this step is controlled by the learning rate:
Too High: The step taken down the mountain may be to large and result in the model going to the other side of the “valley” missing the optimum solution.
Too Low: The model would then be walking down the mountain in very small steps. This would result in the model taking millions of steps in order to go through the training data which would result in the model taking a very long time to train.
To arrive at a stable, accurate set of feature representations within a network, a network must repeat the four steps to arrive at a set of parameters thousands of times going through thousands of batches of data.
Key Types of Neural Networks
It seems that there are many different types of problems in AI and thus different types of artificial intelligence that one can use to solve them. That is, solving the problem of predicting the value of houses for sale in different locations with a program designed to process live video would be a terrible idea. So to begin our look at the different types of neural networks that are the core of much of the artificial intelligence that is currently in use, we will start with the simplest type of neural network and work our way up from there.
Neural Network Type
Primary Strengths
Dominant Use Cases
Feedforward (FNN / ANN)
Simple architecture, quick baseline training
Tabular data analysis, straightforward classification
Convolutional (CNN)
Spatial feature extraction, grid data processing
Image classification, object detection, facial recognition
Recurrent (RNN & LSTM)
Sequential memory, temporal pattern learning
Time-series forecasting, speech-to-text, stock trends
Transformers
Parallel processing, self-attention mechanisms
Large Language Models (LLMs), automated translation
GANs
Generative modeling through competitive training
Synthetic image generation, deepfake detection, art creation
1. Feedforward Neural Networks (FNN)
The simplest and most basic form of neural networks is the Feedforward Neural Network (FNN). These networks are built in a feedforward fashion, meaning that the information in the network moves in only one direction from input to hidden layer(s) to the output(s). This means that there are no feedback loops or cycles in the network. As a result, these types of networks are generally best suited for structured data in the form of tables. Many common problems include customer churn prediction, credit scoring, and standard numeric classification.
2. Convolutional Neural Networks (CNN)
CNNs are usually applied to visual data such as images and even videos. There are 2 main differences that distinguish the CNN from other neural networks. The first difference is how images are passed to the network. Unlike regular networks that flatten 2D images to 1D vectors (which would lose all the spatial relations between the pixels), CNNs use a special kind of layer called a "convolutional kernel." These filters ‘slide’ over the image to scan the entire picture for specific features like edges, lines, shapes and so on.
3. Recurrent Neural Networks (RNN) and LSTMs
When dealing with sequential or time-series data, standard feedforward networks fall short because they treat inputs as independent events. Recurrent Neural Networks (RNNs) introduce internal loops that allow information to persist.
Regular RNNs are not very suitable for dealing with this kind of data because in a regular feedforward network all input is treated independently. There are neural networks that are suitable for processing sequential data, and these are called recurrent neural networks (RNNs). These networks have loops in them, which allow information from previous processing stages to be fed into the current processing stage. However, standard RNNs are not suitable for dealing with long sequences because of the vanishing gradient problem, and for this reason there are specific RNNs, called Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs), which are often used for processing this kind of data. The LSTMs have memory gates, which allow them to remember or to forget part of the context when processing a sentence or when analyzing financial trends.
4. Transformers and Attention-Based Networks
The current state-of-the-art architectures powering ChatGPT, Claude, and Google Gemini all rely on Transformers. First introduced in the groundbreaking 2017 paper “Attention Is All You Need” by Vaswani et al., the core of the Transformer is the self-attention mechanism.
The major difference between the processing of text in RNNs and Transformers is that Transformers process an entire input sequence at once. In addition, Transformers utilize a mechanism called self-attention, which allows a model to determine the importance of every word in a sentence relative to every other word in the sentence, greatly enhancing a model’s ability to handle very complex language understanding.
Real-World Applications of Neural Networks
Industry Impact: Applications of Neural Networks
Healthcare and Medical Diagnostics
Deep neural networks are able to function as powerful diagnostic tools. In order to analyze images of great complexity such as MRI or retinal scans in high resolution, CNNs are able to recognize tumors or even diseases in an early stage of development. They are able to reach the same level of precision as an experienced radiologist. Moreover, deep neural networks are able to speed up the process of drug discovery. This is achieved by predicting the correct folding of a protein as well as the right interactions with other molecules that could be used as therapeutic drugs.
Financial Services and Fraud Detection
- In the financial services sector, for example applications of neural networks include the detection of fraud in real-time while processing transactions. Using hundreds of variables, such as location of the transaction and the device’s footprint, an Artificial Neural Network (ANN) can identify suspicious transactions
- Autonomous Vehicles and Intelligent Transportation
- Self-driving cars rely on a hybrid ecosystem of neural network architectures:
- This enables the CNN to recognize lanes, pedestrians, traffic lights and other street signs in live video from the car’s camera.
- RNNs & LSTMs: They predict the trajectories of surrounding cars over time.
- Reinforcement Learning is used to train Networks that make the decisions for cars in complex traffic situations (e.g. for lane changes or for braking in time).
Natural Language Processing and Generative AI
Your apps and services can learn to understand human language, tone and even intent behind it. This enables virtual assistants such as Siri, Alexa and Google Assistant as well as chatbots for customer service in enterprises to handle any kind of question. Generative Neural Networks such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE) can also generate new content, including emails, human-sounding voice messages, summaries of long documents and even entire applications in code.
Advantages and Challenges of Neural Networks
- While artificial neural networks have the capacity of being very powerful tools, they are not a panacea for all of the problems associated with how software is currently developed. Therefore, in order to use them effectively, one must be aware of their good points and their limitations.
Key Advantages
- Feature Extraction: Deep networks are able to automatically find features that classic machine learning algorithms would need to have explicitly designed.
- High Accuracy on Unstructured Data: Neural networks are highly accurate for unstructured data, such as audio, text, video and more raw sensor data.
- Scalability: The performance of a deep neural network can be increased by increasing the size of the training data set and the computing resources available to train the model.
Current Challenges
Large neural networks require significant computational resources for training, especially on hardware that is particularly well-suited for neural networks (GPUs and TPUs). Training neural networks requires substantial amounts of energy as well.
- The "Black Box" Problem: With so many matrix multiplications, it is almost impossible to follow a neural network’s million+ operations to see where it arrived at its decision.
- Data Dependency: Deep neural networks require thousands to millions of labeled training samples to train a model that generalizes well to new samples (i.e. doesn’t overfit the training samples).
Step-by-Step Practical Example: Building Your First Model
Here is a Python code conceptual example to classify structured data using Keras / TensorFlow.
Python
import tensorflow as tf
from tensorflow.keras import layers, models
# 1. Define a Sequential Neural Network Architecture
model = models.Sequential([
# Input Layer + First Hidden Layer (64 neurons, ReLU activation)
layers.Dense(64, activation='relu', input_shape=(10,)),
# Second Hidden Layer (32 neurons, ReLU activation)
layers.Dense(32, activation='relu'),
# Output Layer (1 neuron for binary classification with Sigmoid)
layers.Dense(1, activation='sigmoid')
])
# 2. Compile Model with Loss Function & Optimizer
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
# 3. Model Summary
model.summary()
Notice how straightforward it is to assemble layers in modern deep learning frameworks: you stack linear layers (Dense), apply activation functions (relu and sigmoid), and compile with an optimization routine (adam).
Accelerating Your AI and Data Science Journey
The same elements are combined in a very straightforward way to create a whole layer of a deep neural network. This includes linear layers (Dense) and activation functions (e.g. ReLU and sigmoid) as well as the compilation with an optimization function (e.g., Adam).
There is still huge demand from established organizations in tech, healthcare, finance, engineering and more for people who have expertise in machine learning and/or neural networks and how they can be put to work in software.
Get the most up-to-date training for artificial intelligence and to start your career in AI or upgrade your current business to work with smart automation today!
- Master Core Fundamentals: Review the fundamental math required for deep learning (linear algebra, calculus, probability) and become proficient in core Python libraries (PyTorch, NumPy, TensorFlow).
- Work on Real-World Projects: Build a portfolio of projects that utilize public datasets from sources such as Kaggle for the purpose of classification and regression.
- Upgrade Enterprise Tech Stack: Find AI expert solution providers to assess how deep learning can be used to automate current workflows in order to unleash new business potential.
Got Questions? Here Are Some FAQs
1. What is Neural Network in simple terms?
A neural network is a computer program which is designed to learn from experience. It is similar to the human brain in that it does not simply follow a set of predefined rules, instead it learns from examples of data and from experience and makes very accurate predictions or decisions.
2. What is the difference between Machine Learning and Deep Learning?
The field of Machine Learning (ML) in general is for computers to learn from experience. Deep Learning (DL) is a subfield of ML. In Deep Learning, deep neural networks with many hidden layers are used to analyze data. They automatically discover the relevant features in the data as opposed to traditional ML methods where a human would manually set up the features of the data.
3. Why are activation functions necessary in neural networks?
Without activation functions a neural network would be able to compute only linear functions of the input (e.g. draw a straight line). The activation functions in a neural network make possible non-linear computation. That is, they enable a neural network to learn to identify complex relationships between inputs and outputs and to produce output that follows a curved line or other complex shape as opposed to simply going up and down in a straight line.
4. How long does it take to train a neural network?
The time needed to train up a neural network depends on several factors including the size and complexity of the network, as well as the size of the training data set. Simple models on small tabular data sets can train up in seconds on a laptop, whereas large language models (such as transformer-based models) can take thousands of GPUs weeks to months to train.
5. What hardware is best for running neural networks?
Small models that run on tabular data can be trained on a CPU-based laptop in a few seconds, whereas large language models such as the 1.3B parameter large language transformer run on thousands of high-performance GPUs for weeks or even months. Typically, hardware that is specifically designed for running neural networks (deep learning) is used for large-scale training. Such hardware consists of Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs).
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