Nestor's summary stated it succinctly. Deep Learning is a Machine Learning technique. The final output/product where the machine learning model is used is commonly referred to as AI.

Another analogy: AI is the car, deep learning is the engine type, machine learning the engine.

The main border is an AI, while Machine learning is a subset of AI on the other hand Deep learning is a subset of Machine learning, so that in simple notation AL=>ML=>DL.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are interconnected but distinct concepts in the field of computer science.

In summary, while AI is the overarching field focused on creating intelligent systems, ML is a subset of AI that focuses on enabling computers to learn from data, and DL is a subset of ML that utilizes deep neural networks to learn complex patterns and representations from data.

AI is the broader field encompassing various techniques and approaches aimed at mimicking human intelligence. Machine learning is a subset of AI focused on enabling computers to learn from data and make predictions or decisions. Deep learning is a further subset of machine learning that emphasizes the training of neural networks with multiple layers to learn complex representations of data.

For example, consider AI as a car >>> Machine Learning as the engine >>> Deep Learning as the pistons. Changing every component of a car requires a thorough understanding of the subject matter. It is only a concept for understanding. For example, to build a new engine, you must have a good understanding of math, probabilities and etc, but if you want to repair the engine, you just need to know the names, applications, and structures and how to repair them.

AI is the overarching field concerned with creating intelligent systems, machine learning is a subset of AI focused on learning from data, and deep learning is a subset of machine learning that specifically uses deep neural networks to learn complex representations of data.

Artificial intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider "smart." Machine learning is a subset of AI that involves the ability of machines to learn from data without being explicitly programmed. Deep learning is a subset of machine learning that involves neural networks with many layers (deep neural networks) that can learn from large amounts of data. So, in essence, deep learning is a type of machine learning, which in turn is a subset of artificial intelligence.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are often used interchangeably but these are not exactly the same terminologies. There are many similarities and differences among these techniques, which are elaborated below:

1. Artificial Intelligence

Artificial means man-made, which is not natural and intelligence is the ability of acquiring and applying knowledge and skills. As the name indicates that AI is the field of science that makes the machine able to mimic or replicate the behaviour of human beings. AI is concerned with building smart machines, which are capable of performing tasks that usually need human intelligence. AI techniques are frequently being applied for the tasks of developing systems endowed with intellectual characteristics of humans, such as the ability to reason, generalize, problem-solving, discover meaning, learning from past experiences, and many more.

2. Machine Learning

ML is a subset of AI, which makes the computer able to learn from data, without being explicitly programmed for a task. ML is concerned with constructing computer programs that are automatically improving with experience. The real power of ML comes from making future predictions based on the received data in the form of observations of real-world events. ML algorithms are capable of learning patterns from the input data and these learned patterns are then used for making informed predictions in the future. Every ML technique can be an AI technique but every AI technique may not be an ML technique, non-ML techniques such as rule-based systems and alpha-beta pruning are also widely used in AI. Some examples of ML algorithms are linear regression, logistic regression, decision trees, random forests, support vector machines, and boosting algorithms. Traditional ML algorithms are useful in many situations, however, they are largely dependent on the quality of features for getting superior performance. The creation of features is also a time-consuming task and needs a lot of domain expertise. Furthermore, with the increasing complexity of the problems, more specifically with the advent of unstructured data such as voice, text, images, and so on, it can be almost impossible to create features for such tasks that represent complex functions. Therefore, there is often a need to find a different approach for solving such complex problems; that is where end-to-end ML approaches come into play.

3. Deep Learning

Deep Learning is a sub-field of ML, DL is just an extension of traditional ANNs. DL is an end-to-end ML architecture that could be applied directly to the data. The main difference between DL networks and ANNs is the depth and complexity of the network. Traditional ANNs have only one hidden layer, while DL networks have more than that. In DL, neural networks may consist of thousands of interconnected neurons (nodes), mostly arranged in multiple layers, where one node is connected to many nodes in the previous layer from where it accepts its input data, as well as being connected to neural nodes in the following layer, to which it sends the output data once it has been processed. One defining characteristic of DL models is the ability to learn features automatically from the input data. Unlike traditional ML, where there is a need to create features manually, DL excels in learning different

hierarchies of features across multiple layers. DL can solve more complex problems by modeling complex patterns than traditional ANNs. Therefore, DL is more widely used nowadays in computer vision and natural language processing applications such as object detection, image recognition, face detection, chatbots, and text generation. DL techniques have made great progress in the past decade. There are many factors that led to this significant rise of DL techniques such as the availability of large quantities of data, improved accuracy, scaling effectively with the data, and more powerful hardware. However, while comparing to the traditional ML techniques, DL needs more training data, more computational power, and more time to train. Moreover, DL methods are also difficult to interpret.

DL is a field of undergoing intense research activities. Researchers are devoted to inventing new neural network architectures that either increase the performance of the previously implemented architectures or tackle new problems. Some of the popular DL techniques are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

Disclaimer: All of the above text is copied from my Ph.D. Thesis, where I have properly cited various sources.

The relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) is often a source of confusion. To clarify the distinctions, lets consider a real-world example from the technology industry:

Imagine you're running a tech company that's developing a suite of smart products.

In summary:

• AI is the whole vision of automated, smart operations across your product line
• ML is the self-adjusting, learning-by-example capability of individual products
• DL is the multi-layered brainpower that teaches your security camera to distinguish a mail carrier from a maple tree...

Learn here the key differences between AI and ML in detail.

https://youtu.be/SJ457febwi4

ad of my understanding

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are terms often used interchangeably, but they refer to different concepts within the field of computational intelligence.

AI: The overarching field focused on creating intelligent systems that can perform tasks requiring human-like intelligence.

ML: A subset of AI that involves developing algorithms that learn from data to make predictions or decisions. systems learn patterns and relationships from data.

DL : A subset of ML that uses the deep neural networks are capable of learning hierarchical representations of data.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interrelated fields within computer science, but they have distinct differences. Here’s a breakdown of each:

Artificial Intelligence (AI)

Definition:AI is a broad field focused on creating systems capable of performing tasks that typically require human intelligence. This includes problem-solving, reasoning, understanding natural language, perception, and the ability to move and manipulate objects.

Scope:

• Encompasses a wide range of techniques and approaches.
• Includes rule-based systems, expert systems, and symbolic AI.

Examples:

• Expert systems that provide decision support.
• Natural language processing (NLP) for understanding and generating human language.
• Robotics for performing physical tasks.

Machine Learning (ML)

Definition: ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data. It emphasizes the use of statistical techniques to enable systems to improve their performance on tasks over time without being explicitly programmed.

Scope:

• Includes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
• Uses algorithms like decision trees, support vector machines, and clustering techniques.

Examples:

• Spam email filtering.
• Fraud detection in financial transactions.
• Recommendation systems (e.g., for movies or products).

Deep Learning (DL)

Definition:DL is a specialized subset of ML that involves neural networks with many layers (hence "deep"). These neural networks are designed to mimic the structure and function of the human brain, allowing them to handle large amounts of data and identify intricate patterns.

Scope:

• Focuses primarily on neural networks, particularly deep neural networks with multiple hidden layers.
• Excels in tasks involving large-scale data and complex patterns.

Examples:

• Image and speech recognition (e.g., identifying objects in images or transcribing speech to text).
• Natural language processing tasks like language translation and sentiment analysis.
• Autonomous vehicles and advanced robotics.

Key Differences

Summary

• AI is the broad goal of creating intelligent machines.
• ML is a way to achieve AI by enabling machines to learn from data.
• DL is a more advanced approach within ML, using neural networks with multiple layers to learn from large datasets and handle complex tasks.