What are Neural and Convolutional Networks?


What are Neural and Convolutional Networks?

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In the world of modern computing, the term “neural networks” has been getting a lot of attention in the past few years. If you are keen to understand what neural networks are and how they work, then this is the perfect place for you to start expanding your knowledge.

What are neural networks?

Neural networks are, basically, computer systems designed to mimic the human brain. They have the ability to learn, understand, and interpret complex patterns, which makes them an important aspect of artificial intelligence (AI) and machine learning (ML).

These networks, much like the neural networks in our brain, are made up of many interconnected processing elements or “nodes”. This layout facilitates pattern recognition, which helps AI systems improve their operations over time. A typical neural network consists of several basic components:

  1. input layer: The input layer is the first connection point for data being fed into the network. It transmits the raw information for further processing.
  2. Hidden layer(s).: After the input layer, the data enters the hidden layer(s). These layers, invisible to external examination, are where the core of the processing takes place.
  3. output layer: The processed information finally reaches the output layer, and provides the final result or prediction.

Each layer is made up of many ganglia or “neurons” connected via “junctions”. Each communication has a weight indicating its importance in the information processing task.

What are neural networks used for?

Neural networks, with their remarkable ability to learn from data and predict outcomes, have become the cornerstone of many contemporary technologies. Its versatility and ability to recognize patterns has paved the way for its application in a range of fields.

One of the most prominent applications of neural networks is machine vision, especially image recognition. Through convolutional neural networks (CNNs), systems can be trained to identify and classify images, such as recognizing faces in an image or identifying objects in a scene. This technology powers many applications, from the automatic tagging of images on social media to diagnosing diseases in medical imaging.

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Neural networks also play a pivotal role in natural language processing (NLP), which enables machines to understand and generate human language. Whether it’s a virtual assistant that understands your voice command, a chatbot that responds to customer inquiries, or software that translates text from one language to another, all of these advances are made possible by neural networks.

How do you train a neural network?

Training a neural network basically means teaching it to make accurate predictions. This involves feeding it data, letting it make predictions, and then modifying network parameters to improve those predictions.

The goal is to minimize the difference between the network’s prediction and the actual output, a term known as “loss” or “error”. The smaller this difference, the better the performance of the neural network.

Step 1: Initialize Weights and Biases

Neural networks consist of neurons interconnected by weights, and each neuron has a bias. These weights and biases are parameters that the network learns during training. Initially, they are set to random values.

Step 2: Feedforward

Feed the network your input data. This data travels across the network from the input layer to the output layer in a process called “feedforward”. Each neuron applies a weighted sum of inputs and biases, followed by an activation function, before passing the result to the next layer.

Step 3: Calculate the loss

After the feed-forward process, the network produces an output. Calculate the loss, which is the difference between this output and the actual value. This loss is calculated using a loss function, which depends on the type of problem you are trying to solve (eg regression, classification).

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Step 4: Backpropagation

Backbreeding is where the magic happens. This process involves adjusting weights and biases to reduce loss. Starting at the output layer, the error propagates back to the previous layers. A gradient of the loss function is computed with respect to each parameter (weight and bias), which indicates the extent to which changing this parameter affects the loss.

Step five: Update weights and biases

The weights and biases are then updated in the opposite direction of the calculated gradient. This is done using an optimization algorithm, which is most popular for Gradient Descent. The size of the steps taken in updates is determined by the “learning rate”, which is a hyperlink parameter that you set.

Step 6: Repeat the process

Repeat steps 2 through 5 for a certain number of iterations or until the loss is less than the desired limit. The number of times the entire dataset is used to update the weights is called an epoch. Training usually includes multiple periods.

What are Convolutional Neural Networks?

Convolutional Neural Networks CNNs are a specialized type of neural network model designed to process network-like data, such as images. These networks are a different kind of the traditional Multilayer Model (MLP) and are mainly inspired by the biological processes of the human brain.

Biological visual cortex

CNNs draw their inspiration from the organization and function of the visual cortex in the human brain. The visual cortex contains small areas of cells that are sensitive to specific areas of the visual field. This concept is reflected in CNNs by applying filters that wrap through the input data.

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convolutional layers

The central component of a CNN is the convolutional layer, which automatically and adaptively learns spatial hierarchies of features. In the convolutional layer, multiple filters move across the image and perform a convolution, in this case a raster product, between the filter weights and the input image. The result of this process is a feature map or a wrapped feature.

aggregation layers

The pooling layer is often added after the convolutional layer to reduce the spatial size, which helps reduce the number of parameters and computational complexity. In addition, it helps the network become more consistent in image size and orientation, thus extracting more powerful features.


At the end of the network, fully connected layers are used, similar to the MLP model. These layers take the filtered, high-level images and translate them into final categories or predictions.

CNNs have been useful in the field of image recognition. Commonly used in applications such as:

  1. Photo and video recognitionCNNs can be used to identify objects, people, and even emotions in photos and videos.
  2. medical image analysisIn the medical field, CNNs are used to analyze images and help diagnose diseases.
  3. Self-driving vehiclesCNNs are used in self-driving cars to detect objects and signs on the road, helping the car to understand the environment and make decisions.
  4. facial recognition systemsCNNs are widely used in facial recognition security systems.

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