Imagine a machine that thinks, learns and adapts like the human brain and discovers hidden patterns within data.
The algorithms of this technology, neural networks (NN), are imitating cognition. We will explore what NNs are and how they work later.
In this article, I will explain to you the fundamentals of neural networks (NN): structure, types, real-life applications, and key terms that define how they work.
Fountain: vitalflux.com
Algorithms called Neural Networks (NN) attempt to find relationships within data, mimicking the operations of the human brain to “learn” from data.
Neural networks can be combined with deep learning and machine learning. That is why it will be good to explain these terms first. Let us begin.
Neural networks form the basis of deep learning, a subset of machine learning. While Machine Learning models learn from data and make predictions, Deep Learning goes deeper and can process enormous amounts of data, recognizing complex patterns.
If you want to learn more about Machine Learning algorithms, read this one.
Furthermore, these neural networks have become an integral part of many fields and serve as the backbone of numerous modern technologies, which we will look at in later sections. These applications range from facial recognition to natural language processing.
Let’s explore some common areas where neural networks play a vital role in improving daily life.
Real-world applications enrich the understanding of neural networks and revolutionize traditional methods across industries with accurate and efficient solutions.
Let’s highlight intriguing examples of neural networks that drive innovation and transform everyday experiences, including types of neural networks.
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ANN (artificial neural networks):
The architecture of the Artificial Neural Network (ANN) is inspired by the biological neural network of the human brain. The network consists of interconnected layers, input, hidden and output. Each layer contains multiple neurons that are connected to each neuron in the adjacent layer.
As data moves through the network, each connection applies a weight and each neuron applies an activation function such as ReLU, Sigmoid, or Tanh. These functions introduce nonlinearity, allowing the network to learn from errors and make complex decisions.
During training, a technique called backpropagation is used to adjust these weights. This technique uses gradient descent to minimize a predefined loss function, with the goal of making the network’s predictions as accurate as possible.
RNA use cases
Customer churn prediction
ANNs analyze multiple characteristics such as user behavior, purchase history, and customer service interaction to predict the likelihood of a customer churning.
ANNs can model complex relationships between these characteristics, providing nuanced insight that is crucial to accurately predicting customer churn.
Sales forecast
ANNs take historical sales data and other variables such as marketing spending, seasonality, and economic indicators to predict future sales.
Their ability to learn from mistakes and adjust to complex, non-linear relationships between variables makes them well suited for this task.
Spam filtering
ANNs analyze the content, context and other characteristics of emails to classify them as spam or not.
They can learn to recognize new spam patterns and adapt over time, making them effective at filtering out unwanted messages.
CNN (Convolutional Neural Networks):
Convolutional neural networks (CNN) are designed specifically for tasks that involve spatial hierarchies, such as image recognition. The network uses specialized layers called convolutional layers to apply a series of filters to an input image, producing a set of feature maps.
These feature maps are then passed through pooling layers that reduce their dimensionality, making the network more computationally efficient. Finally, one or more fully connected layers perform classification.
The training process involves backpropagation, very similar to ANNs, but designed to preserve the spatial hierarchy of features.
CNN Use Cases
Image classification
CNNs apply a series of filters and pooling layers to automatically recognize hierarchical patterns in images.
Their ability to reduce dimensionality and focus on essential features makes them efficient and accurate at categorizing images.
Object detection
CNNs not only classify but also localize objects within an image by drawing bounding boxes.
The architecture is designed to recognize spatial hierarchies, making it capable of identifying multiple objects within a single image.
Image segmentation
CNNs can assign a label to each pixel in the image, classifying it as belonging to a particular object or background.
The network’s granular, pixel-level understanding makes it ideal for tasks such as medical imaging where accurate segmentation is crucial.
RNN (recurrent neural networks):
Recurrent neural networks (RNN) differ in that they have an internal loop, or recurrent architecture, that allows them to store information. This makes them ideal for handling sequential data, since each neuron can use its internal state to remember information from earlier time steps in the sequence.
While processing data, the network takes into account both current and previous inputs, allowing it to develop a kind of short-term memory. However, RNNs can suffer from problems such as vanishing and exploding gradients, making it difficult to learn long-range dependencies in the data.
To address these problems, more advanced versions were developed, such as long short-term memory (LSTM) networks and gated recurrent units (GRU).
RNN use cases
Dictation to text
RNNs take audio sequences as input and produce a text sequence as output, taking into account temporal dependencies in spoken language.
The recurrent nature of RNNs allows them to consider the sequence of audio inputs, making them adept at understanding the context and nuances of human speech.
translation machine
RNNs convert a sequence from one language to another, considering the entire input sequence to produce an accurate output sequence.
Sequence-to-sequence learning capability maintains context between languages, making translations more accurate and contextually relevant.
Analysis of feelings
RNNs analyze text sequences to identify and extract opinions and sentiments.
The memory function in RNNs helps capture emotional accumulation in textual sequences, making them suitable for sentiment analysis tasks.
Looking ahead, the future promises continued advances in neural networks and special use cases. As algorithms evolve to handle more complex data, they will unlock new possibilities in healthcare, transportation, finance, and more.
To learn neural networks, doing a real-life project is very effective. From recognizing faces to predicting diseases, they are reshaping the way we live and work.
In this article, we review its fundamentals, real-life examples like face detection and recognition, and more.
Thank you for reading!
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.
