ANN vs CNN

Artificial neural networks (ANN) and convolutional neural networks (CNN) serve as subtypes of neural networks, which is a deep learning technology. Before differentiating between the two, let's briefly discuss neural networks.

Neural networks

Neural networks are algorithms inspired by the working of a human brain to make decisions and perform tasks. They achieve this by recognizing relations within data like the human brain interprets knowledge.

A neural network consists of nodes connected to each other through synapses in a layered structure. These nodes are analogous to neurons in the brain. They continuously learn from data fed to them and aim to produce results as accurately as possible.

Neural networks are a necessity to be able to make models in deep learning.

Note: Courses we offer in this domain, feel free to check them out!

1. Introduction to Deep Learning & Neural Networks

2. Neural Networks: Beginner to Advanced

ANNs and CNNs are two of the most commonly used neural networks.

Artificial neural networks

Artificial neural networks are general-purpose neural networks that can be applied to various tasks. They work using feedforward and backpropagation techniques.

• Feedforward: ANNs take data through an input node layer and make it pass through single or multiple inner layers until the output node layer is reached. The inner layers are responsible for intelligent data processing and are kept hidden. The information subsequently transforms after passing each layer and finally reaches its output state. Since the data moves forward, this is known as feedforward.

• Backpropagation: Since networks continuously learn, backpropagation aids in this process. Weights are assigned to the nodes. Whenever an error is encountered during training, the information is sent back to the previous node, and the weights are adjusted based on their contribution to the error.

Applications of Artificial Neural Networks

Natural language processing

We use ANNs in NLP tasks like text classification and speech recognition since we can help the models learn to understand and generate human language.

Finance

We can use ANNs to predict stock prices and financial market trends as well as make investment decisions based on patterns.

Healthcare

We can apply ANNs in medical diagnosis and disease prediction as they can help analyze patient data for diagnosis and assist in personalized medicine.

Recommendation systems

ANNs power recommendation engines in various domains, for instance, streaming platforms. This is done by analyzing user preferences to provide personalized recommendations.

Game development

Game development utilizes ANNs to create intelligent non-player characters, commonly abbreviated as NPCs, and optimize game strategies.

Convolutional neural networks

Convolutional neural networks are a specialized type of neural network designed for processing grid-like data like images or sequences. They work using activation functions and various layers.

• Convolutional layers: The data is passed through one or more convolutional layers, and a set of learnable filters are applied. These layers capture patterns by detecting edges, corners, and textures.

• Activation function: Next, an activation function e.g., ReLU is applied to add non-linearity to help make the network learn complex relations between the data and extracted features. An activation function, in simple terms, is like a gate that determines whether a neuron should "fire" or not based on the weighted sum of its inputs.

• Pooling layers: Pooling layers are mainly responsible for downsampling feature maps. In easier words, this means retaining important data while reducing dimensions to reduce computational requirements.

• Fully connected layers: The flattened feature maps are then passed through fully connected layers. They can process high-level features for prediction or classification.

Note:

• Convolutional neural networks also continously improve through backpropation.

• A course on CNNs: Introduction to Convolutional Neural Networks

Applications of convolutional neural networks

Image classification

CNNs excel at image classification tasks, where they can accurately classify images into various categories.

Object detection

Continuing with the first point, CNNs are also widely used for identifying and localizing multiple objects. Let's take R-CNN and YOLO, for example. These CNN-based architectures have advanced object detection performance.

Facial recognition

Another way CNNs can contribute to detection is that they can learn facial features and match them against known identities.

Medical imaging

CNNs have shown promising results in medical image analysis. This is prominent in tasks like tumor detection, segmentation of organs or tissues, and even diagnosing diseases from medical scans.

Video analysis

We can also apply CNNs to video analysis tasks like action recognition, video summarization, and video object tracking.

How well do you know ANNs and CNNs?