Deep Learning And Neural Networks: Introduction To The New Frontier of AI

In this article, we will provide a high-level introduction to deep learning, including its key concepts and some of its most impressive concepts.


Are you tired of using primitive machine learning algorithms that just don't cut it when it comes to solving complex problems? Look no further, because deep learning is here to revolutionize the way we do artificial intelligence. With its ability to mimic the structure and function of the human brain, deep learning allows machines to learn and make decisions on their own, without relying on pre-programmed rules. This technology has already proven its worth in a variety of applications, and we have no doubt that it will continue to push the boundaries of what is possible with AI. In this article, we will provide a high-level introduction to deep learning, including its key concepts and some of its most impressive achievements to date. Get ready to be blown away by the potential of this game-changing technology.

But What Is Deep Learning Anyway?

We are blessed with our intelligence to do various things including having meaningful conversations, reading books, Writing Poems and Novels, Drawing Pictures, and even saying a simple "Hi" to our friends. To be honest, all these activities require a tremendous amount of experience in order for you to understand what is going on around you as well as be able to respond appropriately. All these fancy things are done by our powerful brains of course. But can a machine have this kind of power like humans or can they acquire that? This question puzzled researchers for years and they come to a solution for this. Why can't simulate the working of the brain to a machine? The brain is often complex and we don't even know 100% of the information about the brain until now. But we do know that all these abilities of the brain including reading a book, writing a novel, and having meaningful conversations are the effect of neural activities in the brain. A human brain consists of at least 100 billion neurons. These neurons are connected together to form a network of neurons and they send an electrical signal from one to another when we see, touch, hear, smell, or simply a thought comes in.

Image of Biological Neuron
Image of Biological Neuron

This idea of simulating the properties of the brain leads to a new field in Artificial Intelligence. The "Deep Learning". But when saying new, it is there for years but recently it got more popular and is being adopted almost everywhere from smartphones to self-driving cars. The reason for this is because of the increase in computation capabilities. Implementing Deep Learning algorithms needs a massive amount of computational resources to train them for getting better results. Creating an algorithm that can recognize your faces even needs a certain amount of computational power to work. Fortunately, by now we have all these kinds of resources including powerful CPUs, GPUs and RAM.

The main goal of Deep Learning is to develop Artificial Neurons like Biological Neurons which can be connected together to form an Artificial Neural Network(ANN). Deep learning is all about Building and training ANN to make a machine learning by experience without explicit programming. These networks are able to process large amounts of data and make predictions or decisions based on that data. By training neural networks on vast amounts of data, researchers and practitioners can create deep learning models that are able to make accurate predictions or decisions in a variety of applications, from image and speech recognition to natural language processing and even gaming.

Artificial Neural Network(ANN)
Diagram of an Artificial Neural Network

How do Neural Networks work?

As we said, artificial neural networks are inspired by the biological brain. Just like the brain, an ANN consists of many interconnected processing nodes, or artificial neurons, which are able to process large amounts of data and make predictions or decisions based on that data. Each of the neurons in the network performs some computation and is activated whenever a certain threshold is reached, just like the brain does. This threshold is determined by the inputs and weights of the neuron, which are adjusted during the training process to optimize the network's performance. 

Neural Network Activation - Simple Demo

We know what input is. Anything that is given as training data is known as the input, like an image, or a text, but what about the weights? Weights are basically the hearts of Neural Networks. The weights are the values assigned to each input that determines how much influence it has on the neuron's output. 

Don't be confused by the above image. It's just the way how neurons are activated. From the image, you can see that the weights 0.5 and 0.2 corresponding to the two input neurons make a high value in the top neuron in the hidden layer which is enough threshold for activation. This neuron will get fired(activated) and send the signal to the output neuron, and that's simple right!

By looking at the image you can see one more thing which is the layers, An Artificial Neural Network has one input layer and one output layer, but it can have so many hidden layers. However, from the image, we know that the neuron in the hidden layer is activated because of the higher weights and inputs from the two input neurons. But when the number of neurons and layers grows, we cannot able to understand what happens inside these mysterious hidden layers of the Network. Because of that, we cannot understand why a Neural Network predicts a Cat instead of a Dog when feeding data of images of Cats and Dogs. 

An Artificial Neural Network with 2 or more hidden layers is known as a Deep Neural Network(DNN).

Applications of Deep Learning and Neural Networks

Deep learning has a wide range of applications in various fields, including:

Image and video analysis: 

Deep learning algorithms can be used to process and analyze large amounts of visual data, such as images and videos. This can be used for tasks like object recognition, face detection, and scene segmentation. The greatest example is your smartphone itself, most smartphones today have a face recognition unlock technology that works by identifying the features of your face through Deep Learning algorithms.

Speech and language processing

Why do you feel like talking to a person when you are in conversation with the chatbots available today? Like Google Assistant, Siri, Alexa, OpenAI ChatGPT, etc. The heart of all these assistants and chatbots is built using Deep Learning Algorithms. Deep learning models can be trained to process and understand spoken and written language, allowing them to perform tasks like speech recognition, language translation, and text classification.


No wonder Deep Learning can be used in the field of healthcare. It can be used to analyze medical images, such as x-rays and MRI scans, to detect and diagnose diseases. They can also be used to process electronic health records and other patient data to predict patient outcomes and guide treatment decisions.


Deep learning models can be used to enable self-driving cars and other autonomous vehicles to navigate roads and make decisions in real time. They can also be used to optimize transportation networks and improve traffic flow. One of the greatest examples of intelligent self-driving cars today is the Tesla car.


Deep learning models can be used to analyze financial data and make predictions about stock prices and market trends. They can also be used to detect and prevent financial fraud.


Deep learning algorithms can be used to analyze data from sensors and other sources in manufacturing plants, allowing for more efficient and optimized production processes.

The Future of Deep Learning

Do you think that deep learning will eventually decrease its popularity like other technologies replaced by new technologies? No way, Deep Learning is really powerful that it can be applied to almost all sorts of complex tasks. Increasing the amount of computational power leads to a revolution in the field of Machine Learning and Deep Learning.

The future of deep learning looks bright, with the potential to transform many different fields and industries. As deep learning algorithms and models continue to improve, they will be able to tackle even more complex problems and make more accurate predictions. This will enable deep learning to be applied to a wider range of applications. Additionally, the development of new technologies, such as Quantum Computing, will provide even more powerful tools for training and running deep learning models. All of this suggests that deep learning will continue to play a crucial role in the development of artificial intelligence and other fields, and we can expect to see many exciting advances in the coming years.

Some Challenges and Limitations of Deep Learning

Despite its many successes, deep learning has its challenges and limitations. Some of the key challenges and limitations of deep learning include:
  • The need for large amounts of data: Deep learning algorithms and models require vast amounts of data in order to learn and make accurate predictions. This can be a challenge in situations where there is not enough data available, or when the data is of poor quality or has privacy concerns.
  • The potential for bias: Deep learning algorithms and models can be susceptible to bias if the data they are trained on is not representative of the real world. This can lead to unfair or discriminatory outcomes, and it is an area of active research in deep learning.
  • The lack of interpretability: Many deep learning algorithms and models are complex and difficult to interpret, making it difficult to understand how they arrive at their predictions or decisions. This can make it challenging to verify their accuracy and trustworthiness.
  • The need for specialized hardware: Deep learning algorithms and models can require significant computational resources, and often require specialized hardware, such as GPUs, to run efficiently. This can be a barrier to entry for some organizations and individuals.
Despite these challenges and limitations, deep learning remains a promising and powerful technology, and researchers and practitioners are actively working to address these issues and overcome these limitations.


In conclusion, deep learning is a transformative technology that is revolutionizing many different fields and industries. By allowing machines to learn and make predictions or decisions on their own, without relying on pre-programmed rules, deep learning has opened up new possibilities for artificial intelligence and many other applications. While there are challenges and limitations to deep learning, researchers and practitioners are working to overcome these obstacles and continue to push the boundaries of what is possible with this technology. With its many successes and its bright future, deep learning is a field to watch and one that will continue to have a significant impact on the world around us.

Thanks for reading!

Author: Sidharth