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An Introduction to Neural Networks and Deep Learning: Structures, Types & Limitations

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22nd Aug, 2023
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An Introduction to Neural Networks and Deep Learning: Structures, Types & Limitations

Since you’re reading this article, chances are, you have an understanding of basic machine learning – if not of the technicalities then at least of the theoretical aspects of machine learning. 

Deep Learning is the next logical step after machine learning. In traditional machine learning, the machines were made to learn based on supervision or reinforcement. Deep learning, however, aims to replicate the process of human learning, and allows the systems to learn on their own.

This is made possible using Neural Networks. Think about the neurons in your brain and how they work. Now imagine if they were converted into artificial networks – that is what Artificial Neural Networks are. 

Deep learning and neural networks are going to revolutionise the world we know, and there’s a lot to unpack when it comes to this technology.

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In this introductory article, we’ll give you a brief understanding of deep learning along with how neural networks work, what their different types are, and what are some limitations of neural networks. 

Deep Learning – A Brief Overview

Deep learning can be thought of as a subfield of machine learning. However, unlike any traditional machine learning algorithm or system, deep learning systems use multiple layers to extract high-order features from the raw input that they are fed with. The greater the number of layers, the “deeper” will be the network, and the better will be the feature extraction and the overall learning. 

The term deep learning has been around since the 1950s, but the approaches back then were fairly unpopular. As more research happens in this area, deep learning continues to advance, and today we have sophisticated deep learning methods powered by neural networks. 

Some of the more popular applications of neural networks in deep learning involve face detection, object detection, image recognition, text-to-speech detection and transcription, and more. But we’re only scratching the surface – there’s a lot to discover yet!

So, before you dive deeper into understanding deep learning, we must first begin by understanding what is an Artificial Neural Network in AI. 

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Artificial Neural Network 

ANNs are inspired by how the actual human brain functions and they form the foundation of deep learning. These systems take in data, train themselves to find patterns in the data, and find outputs for a new set of similar data. 

That’s what powers deep learning – neural networks learn by themselves and become stronger in finding patterns automatically, without any human intervention. As a result, neural networks can act as a sorting and labelling system for data.

Let’s understand ANNs in depth by first understanding Perceptrons. 

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ANNs consist of smaller units, like the neural networks in our brain consist of smaller units called neurons. The smaller units of ANNs are called perceptrons. Essentially, perceptron contains one or more input layers, a bias, an activation function, and a final output. 

The perceptron works by receiving inputs, multiplies them by weight, and passes them on through an activation function to produce an output. The addition of bias is important so that no issue occurs even if all inputs are zero. It works on the following formula: 

Y = ∑ (weight * input) + bias

So, the first thing that happens is calculations within the single perceptron. Here, the weighted sum is calculated and passed on to the activation function. Again, there can be different types of activation functions like trigonometric function, step function, activation function, etc. 

Structure of an Artificial Neural Network

To develop a neural network, the first step is grouping different layers of perceptrons together. That way, we get a multi-layer perceptron model. 

Out of these multiple layers, the first layer is the input layer. This layer directly takes in the inputs. Whereas the last layer is called the output layer and is responsible for creating the desired outputs. 

All the layers between input and output layers are known as hidden layers. These layers don’t directly communicate with the feature inputs or final output. Rather, hidden layer neurons from one layer are connected to the other layer using different channels. 

The output that is derived from the activation function is what decides whether a neuron gets activated or not. Once a neuron is activated, it can transmit data to the next layers using the communication channels. Thus, all data points are propagated throughout the network. 

Finally, in the output layer, the neuron with the highest value determines the final output by firing. The value that neurons receive after all the propagation is a probability. It means that the network estimates the output via the highest probability value based on the input it receives.

Once we get the final output, we can compare it to a known label and do the weight adjustments accordingly. This process is repeated till we reach the maximum allowed iterations or acceptable error rate. 

Now, let’s talk a bit about the different types of Neural Networks available. 

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Different Types of Neural Networks

Today, we’ll look at the two most popular types of Neural Networks that are used for deep learning, i.e CNNs and RNNs. 

CNNs – Convolutional Neural Networks

Instead of working with simple 2-D arrays, CNNs work with a 3-D arrangement of neurons. The first layer is called the convolutional layer. Each neuron in this convolutional layer is responsible for processing only a small part of input information. As a result of this, the network understands the entire picture in small parts and computes them multiple times to successfully complete the whole picture. 

Hence, CNNs are extremely valuable for image recognition, object detection, and other similar tasks. Other applications where CNNs have been successful include speech recognition, computer vision tasks, and machine translation. 

RNNs – Recurrent Neural Networks

RNNs came to the limelight around the 1980s and they use time-series data or sequential data to make predictions. Thus, they are handy for temporal or ordinal solutions like speech recognition, natural language processing, translation, and more. 

Like CNNs, RNNs also require training data to learn and then make predictions. However, what makes RNNs different from CNNs is that RNNs are able to memorise the output of one layer and feed it back to the neurons of other layers. As a result, this can be thought of as a feedback network that keeps re-processing information, rather than just feeding the information forward like ANNs. 

Let us now learn the applications of deep learning in various fields.

Applications of Deep Learning in Various Fields

In the introduction to neural networks and deep learning, deep learning has demonstrated remarkable capabilities across various domains, revolutionizing industries and enhancing various applications. Some notable fields where deep learning has made significant contributions include:

1. Computer Vision

Image Classification

Deep learning models excel in recognizing and categorizing objects within images, enabling applications like photo tagging and content-based image retrieval.

Object Detection

Deep learning enables real-time detection of objects within images or videos, which is crucial in surveillance and autonomous vehicles.

Image Segmentation

Deep learning techniques can precisely identify and segment objects within images, aiding medical imaging and computer graphics.

2. Natural Language Processing (NLP)

Sentiment Analysis

Deep learning models can analyze the sentiment and emotion in text, which is valuable in social media monitoring and customer feedback analysis.

Language Translation

Deep learning-powered machine translation systems have greatly improved language translation accuracy and fluency.


NLP-based chatbots use deep learning to understand and respond to natural language queries, enhancing customer service and user interaction.

3. Healthcare

Medical Image Analysis: Deep learning is applied to detect and diagnose diseases from medical images, such as X-rays, MRIs, and CT scans.

Drug Discovery

Deep learning models help drug discovery by predicting molecular interactions and identifying potential drug candidates.

Personalized Medicine

Deep learning aids in tailoring treatment plans based on individual patient data, optimizing healthcare outcomes.

4. Autonomous Vehicles

Self-Driving Cars

Deep learning is vital for perception tasks in autonomous vehicles, including detecting pedestrians, other vehicles, and traffic signs.

Path Planning

Deep learning algorithms assist in identifying safe and efficient routes for self-driving cars.

5. Finance

Fraud Detection

Deep learning models detect fraudulent transactions and activities in financial systems.

Algorithmic Trading

Deep learning enhances the prediction of stock prices and financial market trends.

6. Gaming and Entertainment

 Video Game AI

Deep learning is employed to create intelligent and adaptive game agents that offer challenging and immersive gameplay.

Content Generation

Deep learning models can generate realistic images, music, and videos, creating new possibilities in creative content creation.

7. Industrial Automation

Predictive Maintenance

Deep learning helps predict equipment failures, optimize maintenance schedules, and reduce downtime in industrial settings.

Quality Control

Deep learning enables real-time inspection and defect detection in manufacturing processes.

Limitations of Working with Neural Networks

Neural Network is an area of ongoing research and modifications. So, there are often some shortcomings that are being resolved and rectified to bring sophisticated modifications in the technology. Let’s look at some limitations of Neural Networks: 

Requires a lot of data

Neural Networks work on a huge amount of training data in order to function properly. If you don’t have large amounts of data, it will become difficult for the network to train itself. Further, neural networks have several parameters – like learning rates, number of neurons per layer, number of hidden layers, etc., which needs to be tuned properly to minimise the prediction error while maximising the prediction efficacy and speed. The goal is to allow neural networks to replicate human brain functions, for which it needs a lot of data.

Works mostly as a black box

Because it is often hard to find out how hidden layers work and are organised, neural networks are often seen as a black-box environment. So, if an error occurs, it becomes much challenging and time-consuming to find the cause of the error and fix it. Not to forget, it also becomes quite expensive too. This is one of the main reasons why banks and financial institutes are not yet using Neural Networks to make predictions. 

The development is often time-consuming

Since Neural Networks learn by themselves, the entire process is often time-consuming, apart from being costly, when compared to traditional machine learning methods. Neural Networks are additionally computationally and financially expensive because they need lots of training data and computation power for the learning to happen. 

Training and Optimization Algorithms for Neural Networks

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Training a neural network in deep learning involves adjusting parameters to minimize the error between predicted and actual outputs. This process is crucial for the network to effectively learn and generalize from the training data. Here are some common training and optimization algorithms:

1. Gradient Descent

  • Gradient descent is a widely used optimization algorithm for training neural networks.
  • It works by calculating the gradient of the loss function concerning each model parameter.
  • The gradient represents the direction of the steepest increase in the loss, so the algorithm moves in the opposite direction to minimize the loss.
  • The learning rate, which determines the step size in each iteration, is a crucial hyperparameter affecting the optimization process’s convergence and stability.
  • While gradient descent is straightforward, it may suffer from slow convergence or getting stuck in local minima for complex loss surfaces.

2. Stochastic Gradient Descent (SGD)

  • Stochastic Gradient Descent is a variant of gradient descent that addresses some of its limitations.
  • Instead of using the entire training dataset for each parameter update, SGD randomly selects a mini-batch (subset) of the data.
  • This mini-batch approach accelerates the computation, especially for large datasets, and adds stochasticity, which can help the algorithm escape local minima.
  • However, SGD introduces more noise due to the smaller batch size, which can lead to more erratic updates and slow convergence.

3. Mini-Batch Gradient Descent

  • This gradient descent is the combination of both gradient descent and as well as stochastic gradient descent.
  • It divides the training data into small batches, updating the model parameters based on the average gradient computed from each batch.
  • This prespective strikes a balance between using the entire dataset (as in gradient descent) and a single data point (as in SGD), leading to more stable and efficient convergence.

4. Backpropagation

  • Backpropagation is not an optimization algorithm but a fundamental technique for training neural networks.
  • It involves computing the gradients of the loss function concerning each model parameter using the chain rule of calculus.
  • The gradients are then used to update the parameters during optimization.
  • Backpropagation efficiently calculates the gradient for each layer in a neural network, allowing the network to learn complex representations from data.

In Conclusion

What’s more is that this world is evolving rapidly, with the passing of each week. If you’re passionate about finding out more about deep learning and how neural networks can be made to work, we recommend you check out our Advanced Certificate Programme in Machine Learning and Deep Learning offered in collaboration with IIIT-B. This 8-month long course offers you all you require to kickstart your career – from one-on-one mentoring to industry support to placement guidance. Get yourself enrolled today! 



Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

11. Is deep learning possible without neural networks?

No, Artificial Neural Networks are important for accomplishing deep learning.

22. What are the types of ANNs?

There are various types of artificial neural networks. But the 2 most applied ones are Recurrent Neural Networks and Convolutional Neural Networks.

33. What is the most basic unit of an Artificial Neural Network?

A Perceptron is the most basic unit of ANNs.

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