Table of Contents

Author :

Ampcome CEO
Mohamed Sarfraz Nawaz
Ampcome linkedIn.svg

Mohamed Sarfraz Nawaz is the CEO and founder of Ampcome, which is at the forefront of Artificial Intelligence (AI) Development. Nawaz's passion for technology is matched by his commitment to creating solutions that drive real-world results. Under his leadership, Ampcome's team of talented engineers and developers craft innovative IT solutions that empower businesses to thrive in the ever-evolving technological landscape.Ampcome's success is a testament to Nawaz's dedication to excellence and his unwavering belief in the transformative power of technology.

Deep Learning

What is Deep Learning?

Learn everything deep learning concepts, processes and how to integrate it in your business to boost efficiency and accelerate growth.
Deep Learning- Ampcome
Author :
Ampcome CEO
Mohamed Sarfraz Nawaz
Ampcome linkedIn.svg

Mohamed Sarfraz Nawaz is the CEO and founder of Ampcome, which is at the forefront of Artificial Intelligence (AI) Development. Nawaz's passion for technology is matched by his commitment to creating solutions that drive real-world results. Under his leadership, Ampcome's team of talented engineers and developers craft innovative IT solutions that empower businesses to thrive in the ever-evolving technological landscape.Ampcome's success is a testament to Nawaz's dedication to excellence and his unwavering belief in the transformative power of technology.

Deep Learning

Have you wondered how Google's core products like Google Search, YouTube, and Google Assistant can do accurate speech recognition, language understanding, and video recommendations or how content ranking and user recommendations are so well-optimized across all the Meta apps? 

Well, the secret sauce here is deep learning- a cornerstone of modern artificial intelligence. As a business using AI in full swing, you can’t overlook the fact that deep learning is giving new dimensions to the AI domain and is crucial to achieving remarkable feats in areas like computer vision, machine learning, natural language processing, and critical decision-making. 

By utilizing deep learning the right way, you can tackle intricate problems that were once the exclusive domain of human expertise. Before you rush to adopt it, you must have a deeper understanding of: 

  • What is deep learning?
  • How does it work? 
  • How can you use it for your business? 
  • How is it different from other AI technologies like natural language processing, machine learning, etc? 

This article aims to cover all these areas in detail to equip you with a solid understanding of the core principles behind deep learning, empowering you to evaluate its potential applications within your specific domain.

Must Read: What are AI Agents? How To Build an AI Agent For Your Business?

What is Deep Learning?

Deep learning is a powerful branch of machine learning that has revolutionized the Artificial Intelligence domain. It leverages artificial neural networks, inspired by the structure and function of the human brain, to gain in-depth insight from data.

These neural networks feature various layers of closely connected nodes that can process, exchange, and transform data between each other, enabling you to solve complex representations and patterns. Deep learning algorithms are the founding units of this technology and are considered dependable because they are heavily trained on large-scale datasets. 

What makes deep learning algorithms stand out is their ability to associate distinctive data features with appropriate labels. And once it's trained well, it can use the learning to predict outcomes for new data. Let us explain this with the help of an example. 

Suppose, a deep learning algorithm is at work in an image recognition task. During this task, it will try to learn through associated features such as objects or colors of an object with labels such as a round table or a black color table. Now, it will use this understanding to identify a round table or a black color table in new image input data. 

Evolution of Deep Learning

The types of deep learning advancements we get to see today are the product of years-long research and development. Hers is how deep learning has evolved over the years. 

The first mention of deep learning happened in the 19640s when researchers like Warren McCullough and Walter Pitts proposed the concept of artificial neural networks in their McCulloch-Pitts neuron model. This was a mathematical model, insured by biological neurons, and laid the foundation for artificial neural networks.

However, neural networks gained significant momentum by the 1980s with the development of backpropagation algorithms and the expanded availability of computing power. Researchers like Geoffrey Hinton continued exploring neural networks, proposing the term "deep learning" in 1986.

During 1990-2000, deep learning experienced great growth with the development of key algorithms like LSTMs (Long Short-Term Memory). This was an exclusive algorithm for RNNs or Recurrent neural Networks and was crucial for sequence-based workflows such as language translation. 

By the late 1990s, we experienced the development of powerful GPUs and their unmatched parallel processing abilities accelerated the demand for deep learning. 

At present, machine learning and deep learning have become the flag-bearers of artificial intelligence. Advanced deep learning algorithm applications are extensive including image recognition, speech recognition, natural language processing, and recommendation systems. 

Tech leaders such as Amazon, Meta, Microsoft, and Google have invested extensively to build hardware and software specifically for deep learning workloads and are using it for a wide range of activities. 

Components of Deep Learning Architecture

Deep learning architecture is made of different abstraction layers, featuring specific nodes for different tasks, along with other components. Here is a breakdown of deep learning network architecture. 


Deep learning networks are made of three layers named as:  

  • Input Layer: The first network layer where you feed the raw data into the model. 
  • Hidden Layer: This layer receives the input data, received by the input layer, and processes information at different levels.  Each hidden layer will have processing units, named neurons, to receive the input data. The number of hidden layers, present in a deep learning architecture, decides the complexity of the network. If a network has more hidden layers then the network will become more complex. Depending upon the complexity, a deep learning network can have up to thousands of layers. 
  • Output Layer: This is the final layer and is responsible for generating output. 

Connections and Weights

As we know, each neuron in a deep learning layer is connected to every neuron in the next layer. This neuron connection has weights in the form of dials- controlling the influence of one neuron on another. When deep learning models are trained, these weights are adjusted to improve the output accuracy. 

Activation Functions 

Activation functions play the role of decision gates for deep learning neurons and are responsible for determining when a neuron should fire its signal according to the received weighted input. Additional Components

Alongside, a deep learning architecture also features a loss function- to calculate the time taken by a model to make a prediction- and an optimizer- uses a loss function to adjust the connected weight to keep error possibilities on the lower side. 

How does Deep Learning work?

Deep learning functions using neural networks, which are similar to the neurons of human brains.  These networks are layers of nodes, interconnected to each other. The depth of the network layer is based on the number of layers it has. 

The neural networks of deep learning also send and receive signals from other neural networks. These signals travel between nodes. The weight of the node influences this travel of signals. a heavier-weighted node will have a greater impact on the next layer of nodes it is connected to, as its signal will be amplified more compared to a lighter-weighted node. 

Only weighted nodes comprise the final layer of the neural network and are responsible for generating an output. These nodes leverage a technique called backpropagation to get trained on a specific dataset. As only a large set of data is used for the training of deep learning networks, it’s important to use powerful hardware to process such vast data and perform complex calculations. 

During the data processing, artificial neural networks label the data with the corresponding answer, retrieved using a binary series of True or False questions. Backpropagation techniques propagate the error information, if any, back through the network, adjusting the weights and biases of individual neurons in each layer.

Through this iterative process, the network progressively refines its ability to map inputs to the desired outputs. These neurons then employ the activation features to introduce non-linearity into the network. Once the training is done, the network can predict the output based on the learned features. 

Types of Deep Learning Models

Based on the types of input provided and functions performed, deep learning models are categorized as:

Feedforward Neural Networks (FNNs)

FNNs are one of the simplest artificial neural networks we have. This type of deep learning model involves linear and unidirectional information flow from the input layer. The data flows from one or more hidden layers to the output layers without any cycle or loops. 

This type of deep learning model is commonly used in a wide range of applications such as speech recognition, natural language processing, classification, predictive modelling, and image recognition. 

Recursive Neural Networks (RecNNs)

RecNNs are the advanced versions of FNNs, featuring recursive connections within multiple layers. This added feature makes RecNNs ideal for handling hierarchical data structures like parse trees.

They are very useful for tasks related to parsing, sentiment analysis, and everything else that involves hierarchical data representations.

Convolutional Neural Networks (CNNs)

CNNs are a class of deep neural networks, capable of automatically extracting features from a given input data using convolution operation.  They are widely used in object detection, facial recognition, and image classification tasks. 

AlexNet, VGG-16, GoogleNet, and ResNet are some examples of CNN deep learning. 

Deep Reinforcement Learning

Deep reinforcement learning is a variety of machine learning, commonly used for game playing and robotics. It enables agents to learn the appropriate behaviour in a given ecosystem through end-to-end interaction. With each interaction, it receives a reward or punishment that frames its understanding about the environment. 

Difference Between AI, Machine Learning and Deep Learning

As AI, machine learning, and deep learning are intertwined and share great similarities, it’s obvious to get confused and fail to distinguish between these three. Yet, we will try to explain to you the difference between ML and deep learning, along with AI. 

AI or Artificial Intelligence is the broadest concept among these three and encompasses all attempts to create intelligent machines that can mimic human cognitive functions like learning and problem-solving. It’s not based on a specific model or method. Rather, it represents the overall goal of building artificial machines with human-like competency. 

For example, a chess-playing robot is a type of AI. 

Let’s talk about the difference between ML and deep learning. They both are the subside of AI and focus on a specific model/technique. 

Machine Learning or ML is an AI category that focuses on developing different types of algorithms that empower AI products and solutions. The key aim of these algorithms is to learn from data and provide input, using the last learning.  A specific dataset is fed to the algorithm to reinforce the learning and improve their performance on a given task. 

A reboot recognizing a specific type of image like a table or chair and avoiding it while navigating within a room is an example of machine learning. 

Deep learning or DL is a subfield of machine learning and it uses artificial neural network architecture to learn specific patterns and relationships in a given dataset. It takes relatively a vast amount of data and significant computing power for training. Because of its ability to handle large data sets, it’s useful to solve complex problems and process tedious tasks such as image processing, and natural language processing. 

A self-driving car navigating easily on tough terrain and customizing its speed according to different weather conditions is an example of a deep learning model in real life. 

In a nutshell, you can consider AI as the entire toolbox featuring multiple tools. In this toolbox, machine learning is a set of tools designed specifically for data learning. Deep learning is part of that set of tools and enables data learning for intricate tasks. 

The below-mentioned table summarizes the differences between AI, machine learning, and deep learning.

Feature ΑI Machine Learning Deep Learning
Scope Broader concept encompassing everything related to the development of artificial machines. A subset of AI featuring data learning tools. A sub-field of machine learning enabling data learning for complex workflows.
Key Goal Create intelligent machines with human-like decision making abilities. Data learning. Complex pattern and features learning.
Method Used Wide range of techniques including machine learning and deep learning. Algorithms. Artificial Neural Networks.
Data Dependency Can perform with limited data. Demands more data for better learning. Requires vast amounts of data for enhanced learning.
Processing Power Less demanding. More demanding. Highly demanding.
Training Time Mainly depends on the techniques used. Can be moderate to long. Often takes longer time for effective training.
Model Complexity Can be simple or complex, based upon the techniques used. Varies, but generally less complex because of the use of simple techniques such as linear regression, decision trees, etc. Generally complex because of the use of multiple layers of neural network.
Application Scope Wide range, symbolic reasoning problem-solving. Specific tasks with well-defined patterns. Complex tasks requiring intricate pattern understanding.
Software Libraries Scikit-learn, TensorFlow, PyTorch (general AI). Scikit-learn, TensorFlow, PyTorch (specific ML libraries). Libraries specifically designed for deep learning such as TensorFlow.
Problem-Solving Approach Rule-based, symbolic reasoning. Statistical learning, decision trees. Learning from data representations through multiple layers.

Difference Between Deep Learning and Neural Networks

Though deep learning and neural networks are closely related, they are not the same. They stand apart from each other with certain  obvious differences such as:

Deep learning is a subfield of ML that leverages artificial neural networks for the depth of its layers. Neural networks are the building blocks of deep learning. Inspired by neurons present in human brains, they feature different nodes arranged in a fashion to form layers of deep learning networks. 

Deep learning features multiple layers of neural networks, stacked on each other. A neural network is a single unit that gets paired with different neural networks to form that layer. 

The easiest way to understand the relationship and differences between neural networks and deep learning is to use a house as an analogy. Neural networks in the domain of AI are the basic building blocks, like the walls and windows of a house. A simple house can be built using a few walls and windows. But, deep learning is like building a mansion. It will require more building blocks to create complex structures (hidden layers) on top of the foundation (neural networks). 

In essence, all deep learning models are neural networks, but not all neural networks are deep learning models.

What is Deep Learning Used For?

Because of its unprecedented ability to handle data at large scales and solve complex mathematical representations, deep learning is widely used in computer vision, speech recognition, natural language processing (NLP), and reinforcement learning. Let’s break down the deep learning applications for you.  

Computer Vision 

Businesses building computer vision-related products and solutions can use deep learning models to enable their products to identify and comprehend visual data in a better way. 

  • Self-driving cars, robotics, and surveillance gadgets, developed using deep learning, can locate objects within a given image and video with far better accuracy.   
  • High inaccuracy rate in medical imaging and scanning is leading to faulty or delayed diagnosis. Through the use of deep learning algorithms, the healthcare industry can enhance disease detection and diagnosis accuracy. This algorithm can be trained on massive datasets of medical images, including X-rays, CT scans, and MRIs. By analyzing these images, the algorithms can learn to identify subtle patterns and abnormalities associated with various diseases.
  • Deep learning is fundamentally changing the game of image segmentation across various industries. The retail industry can deploy deep learning through computer vision to segment customers in store surveillance footage, enabling retailers to tailor targeted promotions and product recommendations based on demographics or browsing behaviour.
  • Different types of manufacturing units can use deep learning -enhanced computer vision to automatically detect defects in products on assembly lines by segmenting faulty components within images. This leads to improved product quality and reduced waste.
  • Real-world advanced security systems use deep learning algorithms in computer vision to identify individuals accurately from image and video-based outputs. 
  • Defence systems use DL algorithms to flag areas of interest in satellite images.
  • In security systems and smartphones, deep learning algorithms can accurately identify individuals from images and video.

Natural Language Processing (NLP)

Deep learning breathes new life into NLP by enabling machines to understand and process human language in a better manner. 

  • Chatbots powered by deep learning such as Alexa and Cortana can conduct a natural conversation with humans for a long time and answer queries with full accuracy. Deep learning also enables sentiment analysis using customer reviews, emails, and social media posts. This allows businesses to gauge customer satisfaction, identify areas for improvement, and proactively address negative feedback. 

Do You Know: Airlines like Delta use NLP to analyze customer feedback and improve their flight booking experience.

  • The finance industry can leverage deep learning NLP to identify patterns indicative of fraudulent activity.  You can deploy deep learning-based NLP solutions to detect suspicious money transfers and protect your customers from financial crimes. You can even build an AI agent with deep learning power to analyze vast amounts of financial news and social media data to identify trends and predict market movements. 

Must Read: AI Agents in Finance: All You Need to Know 

  • Deep learning in NLP is breaking down language barriers in the media and entertainment industry. Services like Google Translate now use deep learning models to achieve near-human-quality translation, enabling seamless communication across languages. 
  • Giants of the media and entertainment industry such as Netflix, YouTube, and Spotify are already using deep learning to understand users’ preferences in movies, music, and videos, and make recommendations accordingly. 

Reinforcement Learning

Deep learning and reinforcement learning, when joined as deep reinforcement learning (DRL), are creating a powerful one-two punch for various industries. 

Imagine robots that can handle delicate tasks or navigate complex environments with human-like agility. Yes, this is possible through the integration of deep learning in reinforcement learning.  DRL algorithms enable robots to learn from trial and error through simulation, continuously improving their motor skills and decision-making in real-world scenarios. 

  • Self-driving cars can learn complex driving behaviors like navigating to traffic circles, changing speed according to diverse weather conditions, and merging into highways. 
  • The gaming industry is hugely influenced by DRL algorithms as they are used for the development of AI  agents as opponents in video games and high-skilled games like Go and Chess. 
  • DRL also empowers the development of trading algorithms that can learn from market fluctuations and adapt their trading strategies in real time. 
  • The use of DRL leads to the development of AI agents to control highly complex systems such as power grids, supply chains, and traffic lights. 

Speech Recognition 

Deep learning algorithms are bringing revolutionary changes in speech recognition by improving its accuracy, adaptability, and user-friendliness. Here are some of the most common and widely acceptable use cases of deep learning in speech recognition. 

  • Doctors can dictate notes directly into their electronic health records with the help of deep learning, saving time and improving accuracy 
  • Chatbots and virtual assistants, powered by deep learning, can understand natural language, allowing for more efficient and personalized interactions.
  • Deep learning is fine-tuning voice commands in cars, reducing driver distraction. 

Do You Know: This Healthcare Industry Player has an AI-solution for all its diverse needs, resulting in simplified operations. 

What is Reinforcement Learning?

Reinforcement learning or RL is also a domain of machine learning, focusing on taking appropriate action to make sure that a reward in a given situation is the maximum possible.  It differs from supervised learning. In supervised learning, the training data already has the answer key. 

But, reinforcement learning takes actions based on the given situation. If training data is absent then it will learn from the experience. The basic learning method used in this type of learning is a trial-and-error method. 

Here are a few points to remember about reinforcement learning. 

  • There should be specific input to start the initial state. 
  • In a given model, there are possibilities of multiple outputs of a given problem.  
  • The model training is mainly based on the provided input. When the user decides on a suitable reward/punishment, the model returns to a state. 
  • The model keeps on learning through trial-and-error methods. 
  • The model decides the fitting solution, based on the maximum reward.

What are Generative Adversarial Networks?

Generative Adversarial Networks or GANs are another machine learning class, used for generative modeling. They are made up of two types of neural networks-  a generator and a discriminator. These two neural networks compete against each other in a zero-sum game framework. 

The key function of the generator is to generate hyper realistic data samples like images and random noises as input. Using this input, makes the discriminator push into critical thinking and compels it to consider those random inputs as real.  

The job of a discriminator is to label the data as fake or real. These two- a generator and a discriminator- are trained together. Through the learning process, the generator keeps on generating more realistic samples to fool the discriminators in a better manner. On the other hand, the discriminator tries to get better at identifying the fake inputs.  

This is a never-ending combat that continues between these two neural networks and results in more optimized outputs. 

GANs have been successfully applied to many generative tasks like image synthesis, image-to-image translation, text-to-image generation, and more. They are a powerful approach for learning generative models in an unsupervised manner.

What is a Graph Neural Network?

Graph Neural Network or GNN in deep learning refers to a model designed to process data in the form of graphs. GNNs are widely used where non-Euclidean data structures are used in highly complex relationships and interdependencies between objects. 

The foundational idea behind GNNs is to gain an understanding of node representations by extracting data from the node’s neighbourhood. Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSage are recently proposed GNNs in deep learning. 

What is Natural Language Processing?

Natural Language Processing or NLP  is a field that combines computer science, linguistics, and machine learning to study how computers and humans communicate in natural language. The prime aim of NLP is to help computers or machines to understand human language more naturally. 

It enables machines to manipulate language in written, spoken, and organized forms, allowing them to perform tasks like text translation, voice recognition, sentiment analysis, chatbots, and text classification. 


Deep learning in AI and machine learning is bringing outstanding revolutions in image and speech recognition, natural language processing, and many other domains. However, taming this technology and using it for your benefit is a challenging job as you need to manage large amounts of high-quality data to train the model accurately, acquire the right type of hardware, and have the capabilities to intercept complex deep learning models. 

Ampcome is here to help in all these ventures. With its skilled AI developers, this leading AI development company ensure that you’re able to collect, preprocess, and label data in the right manner, implement techniques like feature importance, saliency maps, or model explainability methods to provide insights into the models' workings, and have domain expertise to devise customized deep learning models.   

They have a greater understanding of neural networks such as CNNs, RNNs, and Long Short-term Memory (LSTM) and will use them to offer timed and tailored AI solutions with deep learning abilities. 

Let Ampcome be your partner in innovation and success. Contact us today to elevate your AI initiatives to new heights.


What's deep learning in AI?

Deep learning in AI refers to a sub-field of machine learning featuring artificial neural networks with multiple layers to learn complex patterns from vast amounts of data. It’s used to handle complicated tasks such as NLP and computer vision. 

How is deep learning used in the real world?

Deep learning algorithms are used in computer vision, NLP, and reinforcement learning-related workflows such as image recognition, image segmentation, image classification, better navigation in self-driving cars, sentiment analysis in chatbots, and facial recognition. 

How is deep learning related to machine learning?

Deep learning is a part of machine learning- a broader AI field encompassing multiple techniques.  Machine learning enables machines to learn from a given data and make accurate predictions whereas deep learning aims to understand complex patterns and data relationships. Machine learning uses algorithms while deep learning is based on artificial neural networks. 

How is deep learning related to conversational AI?

Deep learning and conversational AI are closely related to each other. Conversational AI leverages deep learning techniques, such as neural networks, to process and understand human language inputs, enabling machines to engage in human-like conversations.

Deep learning models such as recurrent neural networks (RNNs) and natural language processing (NLP) empower conversational AI systems to interpret the intent behind user queries in a better manner and generate appropriate responses. 

How does the deep learning model work?

A deep learning model uses artificial neural network structures similar to neurons of human brains to -perform sophisticated computations on large amounts of data. The model has three layers, input, hidden, and out layers. The input layer receives inputs in the form of nodes, hidden layers process the data, and output data delivers the result. 

How does deep learning work in AI?

Deep learning in AI works by utilizing artificial neural networks to process and analyze data, mimicking the structure and function of the human brain. These neural networks consist of multiple layers of nodes that work together to learn from examples and extract features from the data. 

How many deep learning models are there?

There are many types of deep learning models such as Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and others.

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