What Is Deep Learning | AI Mimicking The Human Brain 

    Are you a fan of self-driving cars, robotics, or how AI detects cancer? You must be wondering, how are machines developing human abilities so efficiently? Well, the same way we teach a child, we’re teaching these machines. It’s, however, a bit more complicated than that. There are multiple ways to train a machine to mimic human behavior one of which is deep learning. So, what is it and how does it work? 

    What Is Deep Learning?

    Deep learning (DL) is a subset of machine learning (ML), which is a subset of artificial intelligence (AI). Artificial intelligence is the simulation of human intelligence by machines. Human intelligence is what gives us the ability to think, analyze, reflect, solve problems, etc. How can you look at a dog and know it’s a dog? Thanks to your brain. 

    Computer systems don’t have the facilities, meaning a brain, to do that. Machine learning is training computers to think like humans. This is done through the use of data and algorithms in a way that systems automatically learn and improve from experience, exactly like how a child learns not to eat jalapenos because they stung the first time. 

    So, what is deep learning? Deep learning is a type of machine learning inspired by the structure of the human brain. The latter is made up of billions of interconnected neurons. Deep learning refers to creating an artificial neural network within computer systems so they too can tell a dog from a cat. 

    What Is Artificial Intelligence

    Deep Learning vs. Machine Learning

    If deep learning is a subset of machine learning, how are they different? They differ by the algorithmic process, type, and amount of data used.  

    Let’s say you want your computer to detect cars. If you apply machine learning algorithms, you must feed the system the data and features manually. This means you’d have to specify all features that make up a car (eg. tires, windows, lights, etc.). This is called labeled and structured data.

    However, if you apply deep learning algorithms, you just feed the system different pictures of cars. The DL algorithm will automatically extract the features by itself. This is called unstructured data

    Machine learning can use unstructured data but it does some pre-processing to organize it into a structured format. 

    Afterward, both DL and ML automate the process of classifying the data (eg. what’s a car and what’s not a car).Machine Learning vs Deep Learning

    So, deep learning extracts the features by itself. However, this requires very large datasets to improve accuracy. Machine learning requires less data and cannot handle the large amount that DL can. Machine Learning vs Deep Learning Comparison Table

    How Does Deep Learning Work

    We got an answer to what is deep learning, now let’s see how it works. Deep learning algorithms are inspired by the structure of the human brain. The latter is essentially made up of billions of neurons that connect via synapses. When one neuron is sufficiently stimulated, it passes the message to the other neuron. This is the structure that allows us to see, think, analyze, react, solve problems, and so on. 

    Deep learning, at its core, mimics this by creating artificial neural networks. And, we train these networks to “think” very similarly to how humans do. This allows these machines to conduct human behavior such as problem-solving and pattern recognition.

    What Are Artificial Neural Networks?

    Neural networks (NN) are the core unit of deep learning. They’re computational models inspired by the human brain’s interconnected neurons. We train NN by feeding them large amounts of data.

    Neural Networks’ Structure

    The neural network is made up of interlinked ‘neurons’ structured into multiple layers. Neurons of one layer connect with those of other layers through channels. Below is the structure of a NN:Neural Network

    • Neuron: Mathematical function that receives an input, processes it and generates an output. It is the fundamental element of a neural network. 
    • Input layer: The first layer of the neural network is made up of neurons that take in the input (data).
    • Output layer: The last layer of the neural network is made up of neurons that give the model’s output.
    • Hidden layers: The layers in between the input and output layers. 
    • Channels: How layers interact with each other
    • Weight [W1]: Parameter within the NN that signifies how much influence the input is gonna have on the output.
    • Bias [B1]: Parameter that indicates how easy it is to get a node to fire.

    How Does A Neural Network Work?

    Let’s say we want our neural network to recognize the number 8, how would it work? According to the concept of deep learning, we feed the network the data and train it to identify the number 8. 

    The steps are as follows:How Does A Neural Network Work

    1. You gather the data you want to train the model with. In this case, it would be pictures of numbers. Each image is present as 28×28 image pixels which amounts to a total of 784 pixels.
    2. You feed each of these 784 pixels to a neuron in the input layer of the neural network. 
    3. Channels will randomly assign weights to each input.
    4. A bias is then added to each input.
    5. In order for the information to pass through, the following formula is applied: y = sum (weight * input) + bias
    6. This is then applied to a mathematical function called the “activation function”. The results then determine if the neuron gets activated or not.
    7. The final output will show up in the layer of the NN which is the output layer. 
    8. We then compare the final output with the required output, the number 8. 
    9. We keep adjusting the weights and biases until we’re very close to 100% accuracy. 

    Deep Learning Algorithms

    What are the algorithms that make up deep learning? There are different types of deep learning algorithms but the most widely used forms are feedforward neural networks (FFN), convolutional neural networks (CNN), recurrent neural networks (RNN), and the generative adversarial network (GAN).

    Feedforward Neural Networks (FNNs)

    A feed-forward neural network is the most basic form of NN consisting of two layers: an input layer and an output layer. Sometimes hidden layers are present in between but not necessarily as it depends on the use. . In this type of network, the data is fed in one direction only and never backward. Feed-forward neural networks are majorly used for classification. 

    It works exactly like how we mentioned above, inputs are assigned weights and biases and then get processed through an activation function. The result of the latter determines if the neuron fires or not. Feed-forward Neural Network

    Convolutional Neural Networks (CNN)

    Convolutional neural networks are made of multiple layers and are commonly used for natural language processing (NLP) and object recognition. With each layer, the network increases in complexity. 

    For example, the first layers focus on simple features such as colors and edges. However, deeper layers start to focus on larger elements or shape of the objects until it finally identifies the object itself. 

    Recurrent Neural Networks (RNN)

    Recurrent neural networks are characterized by their “memory” as they use information from previous inputs to influence the current inputs and outputs. So, the information cycles through a loop, unlike feed-forward networks where the information goes in a forward direction only.

    This deep learning algorithm is mainly used in language translation, natural language processing (NLP), speech recognition, and image captioning.Recurrent Neural Network

    Generative Adversarial Network (GAN)

    A generative adversarial network is a model where two neural networks compete with each other to enhance the accuracy of their predictions. The two networks in a GAN are called a generator and a discriminator where the first is a convolutional neural network and the latter is a deconvolutional one. 

    The generator artificially manufactures outputs that can be easily mistaken for real data. The discriminator then has to identify which of these outputs have been artificially created. This type of network is mainly used in anomaly detection, data augmentation, text-to-image, and image-to-image translation. 

    Where Is Deep Learning Applied?

    Now that you understand what is deep learning, you might be wondering if anyone uses it. Well, deep learning is part of our day-to-day life and is more common than you might think. You have come across it many times without even realizing it. The following are some of the many deep-learning applications. 

    Customer Support

    Multiple companies and organizations have integrated deep learning technology into their customer support processes. Many use chatbots to help these organizations offer support to their customers. Moreover, there are virtual assistants like Siri and Alexa that further help these companies. 


    The healthcare industry has been applying deep learning in an incredibly useful way. Image recognition applications aid professionals in helping them analyze and assess images with less data. Some deep learning applications are even detecting cancer early on.  

    Law Enforcement

    Deep learning algorithms can analyze data to identify dangerous patterns that can indicate fraudulent or criminal activity. This increases efficiency and effectiveness in the investigation process. 

    Financial Services

    Financial institutions use deep learning algorithms to analyze trending stocks, assess risks in the industry, and detect fraud. These algorithms can even help manage credit and investment portfolios for clients. 


    Have you ever wondered how Netflix, Amazon, and YouTube suggest content that you’re interested in? They all use deep learning to analyze your browsing history, interests, and behavior to present you with recommendations that fit your taste. 

    Self-driving Cars

    If you’re a fan of sitting back and letting your car drive itself, you owe a big thank you to deep learning algorithms. You can train cars to drive and even perform much better and safer than humans do. Of course, this requires a lot of data and processing powers but it has already been applied by multiple car brands. 

    Advantages and Disadvantaged of Deep Learning

    Now that we’ve got all points covered from what is deep learning, to how it works, and to how it is applied, it’s time we dig a little deeper. Everything has its own set of pros and cons, so what are deep learning’s positives and negatives? 

    Deep Learning Benefits

    It goes without saying that deep learning algorithms have revolutionized the tech space, and the world in general. Deep learning offers a lot of advantages in comparison with ML methods like automatic feature extraction, handling large data, structured and unstructured data, sequential data, missing data, and non-linear relationships, among others. 


    • Automatic feature extraction: Deep learning algorithms can extract features from the presented data. So, you don’t have to manually feed them the features like the case of machine learning. This is mainly useful in tasks where features are hard to define like image recognition. 
    • Handling large data: Deep learning algorithms can process large and complex data sets that would be difficult in other machine learning algorithms. Hence DL is capable of automating the feature extraction process.
    • Handling structured and unstructured data: DL algorithms can handle both structured and unstructured data such as images, text, and audio.
    • Processing sequential data: Some DL algorithms like recurrent neural networks specifically handle sequential data such as time series, speech, and text. These algorithms are characterized by having a memory allowing them to make predictions and decisions based on prior inputs. 
    • Handling missing data: DL algorithms can still make accurate predictions even if certain data is missing or incomplete. 
    • Handling non-linear relationships: Traditional machine learning algorithms are based on linear relationships whereas deep learning algorithms can work with non-linear processes. 
    • Enhanced performance: Deep learning has helped solve a lot of problems including image and speech recognition, natural language processing, and computer vision.
    • Predictive model: You can use DL algorithms to make predictions which is very handy to organizations in the planning and strategic decision-making processes. 
    • Scalability: Deep learning models can easily scale and they’re built to handle large data sets and can be established on cloud platforms and edge devices.

    Deep Learning Limitations

    While deep learning has many advantages, it also has some limitations like high computational cost, dependence on data quality and data presented, overfitting, difficulty of interpretability, black box models, etc. 


    • High computational cost: Deep learning models can process large and complex data but this requires significant computational resources including advanced GPUs. These hardware requirements are expensive and time-consuming. 
    • Dependence on data quality: Naturally, DL models highly depend on the quality of data they are presented with. If the data is rigged, missing, or incomplete it can majorly affect the model’s performance. 
    • Limitation to presented data: DL algorithms can only make predictions and decisions based on the data they’ve been trained on. Therefore, they may not be able to generalize to new situations they have not been presented with during the training period. 
    • Overfitting: This is the case when the model performs greatly on training data and very poorly on new unseen data. Overfitting is a common problem in deep learning algorithms especially within large neural networks. 
    • Difficulty of interpretability: Some deep learning models, especially those with various layers, can be hard to interpret. 
    • Data privacy and security issues: Deep learning models require large amounts of data which poses concerns regarding data privacy and security. This presents risks of identity theft and financial loss. 

    Moreover, a common problem within DL is what’s commonly known as “the black box problem”. So, what is deep learning’s black box problem? It refers to the inability or difficulty to understand how the model is making predictions. This is an issue because it makes us unable to update the model when it presents us with undesirable outputs. 

    However, new solutions continue to rise to enhance the performance of deep learning algorithms.


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