Deep Learning and Neural Networks are closely related but distinct concepts. Neural Networks are computational models inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process input data to produce outputs. Deep Learning, on the other hand, refers to the use of multi-layered (deep) Neural Networks with numerous hidden layers, enabling the system to automatically extract complex features from raw data. While all Deep Learning models are Neural Networks, not all Neural Networks qualify as Deep Learning—shallow networks with only one or two hidden layers fall outside this category. Deep Learning's strength lies in its ability to handle vast datasets and solve intricate problems, whereas simpler Neural Networks are often limited to less complex tasks. Thus, the distinction lies in scale, complexity, and application scope.

Deep learning concerns the training process of neural networks with deep architres, i.e., networks with at least two hidden layers.

Generally speaking, an artificial neural network (ANN) is designed to represent a complex (non linear) hypothetical function that connects the inputs of a training database to the corresponding outputs. It consists of several interconnected computing units, called neurons, divided into an input layer, an output layer, and one or more hidden layers.

Regardless of its size, training an ANN involves adjusting its learnable parameters to minimize a measure of its overall error across the entire training data set. These parameters represent the weights of the synaptic connections and the bias of the individual neurons.

Theoretically, according to the universal approximation theorem (https://www.geeksforgeeks.org/universal-approximation-theorem-for-neural-networks/), any complex function can be approximated using a single hidden layer network. In practice, however, it has been proved that the use of deep architectures enables more efficient learning in many real-world problems, including image analysis (computer vision) and natural language processing.

For these types of problems, depth enables the construction of more efficient models, capable of more intuitively learning rich hierarchies of meaningful data representations.

In a very simple way:

Artificial Neural Networks (ANN):

A biologically inspired model which, in a simple way, emulates the structure and function of the human brain. It contains neurons and their connections (synapses). Strenght of the connections are represented by weights. The neuronal network is generally organized in different layers, from an input layer to an output layer.

Deep Neural Networks (DNN):

These are multi-layered artificial neural networks, usually with many nodes/neurons and consequently many connections/synapses. These so-called DNNs are capable of learning very complex patterns.

Learning in artificial neural networks:

This is fundamentally the process (algorithm) by which the weights of the connections between the nodes/neurons are adjusted. For example, in supervised learning, the aim is to minimize the error between the predicted and actual values. Learning in ANNs and DNNs may differ due to the complexity.

Deep Learning:

This is essentially a machine learning model based on the use DNNs and a set of devoted algorithms that exploit their intrinsic complexity (use of hierarchical data, data transformation and processing to learn data features, etc.).

Deep Learning vs. Neural Networks: Understanding the Differences

Neural networks (NNs) and deep learning (DL) are often used interchangeably, but they are not the same. Neural networks are computational models inspired by the human brain, consisting of interconnected layers of neurons (input, hidden, and output layers) that process data. These networks can be simple, with just one or two hidden layers, making them suitable for tasks like basic classification or regression. However, traditional neural networks often require manual feature engineering, meaning humans must preprocess and select relevant data features for the model to work effectively.

Deep learning, on the other hand, is a specialized subset of machine learning that uses deep neural networks—neural networks with many hidden layers (sometimes hundreds). These additional layers allow deep learning models to automatically extract and learn complex features from raw data, eliminating the need for extensive manual feature engineering. Because of their depth, deep learning models excel at handling large-scale, high-dimensional data such as images, speech, and text. However, they require significantly more computational power (often needing GPUs or TPUs) and vast amounts of training data to perform well.

A key distinction lies in their applications. While traditional neural networks work well for simpler tasks like predicting housing prices or spam detection, deep learning powers advanced AI systems such as self-driving cars (using CNNs for image recognition), language models like GPT-4 (using transformers), and voice assistants (using RNNs/LSTMs for speech processing). Deep learning’s ability to automatically learn hierarchical representations makes it superior for tasks involving unstructured data, whereas shallow neural networks are more efficient for structured, smaller-scale problems.

In summary, all deep learning models are neural networks, but not all neural networks are deep learning. The choice between them depends on the problem’s complexity, data availability, and computational resources. For straightforward tasks with limited data, a basic neural network may suffice, while deep learning is the go-to solution for cutting-edge AI applications requiring sophisticated pattern recognition.

Thank you for this explanatory video. I think this link also explains the differences between deep learning and neural networks.

Deep learning and neural networks are closely related concepts within the field of artificial intelligence, but they are not exactly the same. Think of it this way:

• Neural Networks are the foundational building blocks. They are computational models inspired by the structure of the human brain, consisting of interconnected nodes (neurons) organized in layers. These networks learn by adjusting the connections (weights) between the neurons based on the data they are fed. A simple neural network might have an input layer, one or more hidden layers, and an output layer.
• Deep Learning is a subset of machine learning that utilizes neural networks with multiple layers (hence the term "deep"). These deep neural networks are capable of learning intricate patterns and hierarchical representations from large amounts of complex data. The "depth" of the network, referring to the number of hidden layers, allows deep learning models to automatically learn features from raw data, reducing the need for manual feature engineering.

There are many ways to build an intelligent system, with some of the most notable methods including Fuzzy Logic, Expert Systems, Machine Learning, and many others. Deep Learning is a subdomain of Machine Learning that revolves around various AI development techniques based on neural networks. Neural networks, in turn, are computerized replicas of the human brain. The human brain is composed of trillions of tiny neurons, which are fundamental to understanding, thinking, and functioning as humans. A perceptron is a mathematical model of a human brain neuron and serves as the foundation of neural networks.