Understanding the most recent advances in Artificial Intelligence (AI) can be overwhelming, but if you're interested in learning the basics, many AI innovations can be boiled down to two concepts: machine learning and deep learning.
Most people associate the buzzwords machine learning, deep learning, and artificial intelligence with one another. However, all of these terms are distinct but related to one another.
There are numerous examples of deep learning and machine learning. It's what makes self-driving cars a reality, how Netflix predicts which show you'll want to watch next, and how Facebook recognizes whose face is in a photo.
Machine learning and deep learning may appear to be interchangeable buzzwords, but they are not. So, let's take a closer look at the key differences between machine learning and deep learning.
What is Machine Learning?
When computers learn from data, this is referred to as "machine learning." It refers to the area of computer science and statistics where algorithms are used to perform a specific task without being explicitly programmed; rather, they recognize patterns in data and make predictions when new data arrives.
The learning process of these algorithms can be either supervised or unsupervised, depending on the data used to feed the algorithms. An example of a traditional machine learning algorithm is a simple linear regression algorithm.
In the first step, define a function, such as income = y + x * years of education. Then, feed your algorithm some training data. Then, let your algorithm draw the line, for example, using an ordinary least squares (OLS) algorithm.
While this example appears simple, it qualifies as machine learning – and, yes, ordinary statistics are the driving force behind machine learning. Without being explicitly programmed, the algorithm learned to make predictions based solely on patterns and inference.
How does Machine Learning Work?
Machine learning is a type of artificial intelligence (AI) that teaches computers to think as humans do: by learning from and improving on past experiences. It works by analyzing data and identifying patterns with little human intervention.
Machine learning can automate almost any task that can be completed using a data-defined pattern or set of rules. This enables businesses to automate processes that were previously only performed by humans, such as customer service, bookkeeping, and resume review.
Techniques used by Machine Learning :
You can use Supervised Learning to collect data or generate a dataset from a previous Machine Learning deployment. Supervised Learning is exciting because it works in the same way that humans do.
Unsupervised machine learning enables the discovery of previously unknown patterns in data. In unsupervised learning, the algorithm attempts to learn some inherent structure to the data using only unlabeled examples. Clustering and dimensionality reduction are two common unsupervised learning tasks.
Also Read | Types of Clustering Algorithms
What is Deep Learning?
Deep Learning is a subfield of machine learning concerned with artificial neural networks, which are algorithms inspired by the structure and function of the brain. Deep learning is a machine learning technique that teaches computers to do what humans do instinctively: learn by doing.
Deep learning is the process of teaching a computer model to perform classification tasks directly from images, text, or sound. Deep learning models can achieve cutting-edge accuracy, even outperforming humans in some cases. A large set of labeled data and multi-layered neural network architectures are used to train models.
Deep learning is a key technology behind self-driving cars, allowing them to recognize a stop sign or distinguish between a pedestrian and a lamppost.
It is essential for voice control on consumer devices such as phones, tablets, televisions, and hands-free speakers. Deep learning has received a lot of attention recently, and for good reason. It is achieving previously unthinkable results.
Also Read | Deep Learning Techniques
How does Deep Learning Work?
Similar to how the human brain is made up of neurons, neural networks are made up of layers of nodes. Individual layer nodes are connected to nodes in neighboring layers. The network's depth is indicated by the number of layers. A single neuron in the human brain receives thousands of signals from other neurons.
Signals in an artificial neural network are assigned weights as they travel between nodes. A node with a higher weight has a greater impact on the nodes below it. The last layer combines the weighted inputs to produce an output.
Because deep learning systems process a large amount of data and perform several complex mathematical calculations, they require powerful hardware. Computations for deep learning training can take weeks. They can, however, be performed even with such advanced hardware.
To produce accurate results, deep learning systems require a large amount of data; thus, information is fed as large data sets. Artificial neural networks can classify data based on the answers to a series of binary true-or-false questions involving highly complex mathematical calculations when processing it.
A facial recognition program, for example, learns to detect and recognize edges and lines on faces, then more significant parts of faces, and finally overall representations of faces. The program trains itself over time, increasing the probability of correct answers. In this case, the facial recognition program will correctly identify faces over time.
The Difference Between Machine Learning and Deep Learning
The first step toward understanding the distinction between machine learning and deep learning is to recognize that deep learning is a subset of machine learning.
Deep learning, in particular, is thought to be an evolution of machine learning. It employs a programmable neural network, which allows machines to make accurate decisions without the assistance of humans.
Deep learning is a subset of machine learning. While both are classified as artificial intelligence, deep learning is the driving force behind the most human-like AI.
Machine learning, while requiring a large amount of data, can also work with smaller amounts of data. Deep Learning algorithms rely heavily on large amounts of data, so we must provide a large amount of data to them in order for them to perform well.
To produce results, machine learning necessitates more ongoing human intervention. Deep learning is more difficult to set up but requires little intervention after that.
In machine learning, algorithms are used to parse datasets, learn from them, and make informed decisions based on what they have learned. Deep learning employs layers of algorithms to create an "artificial neural network" capable of self-learning and making intelligent decisions.
Machine learning systems can be quickly set up and run, but their power may be limited. Deep learning systems take longer to set up but produce results almost instantly (although the quality is likely to improve over time as more data becomes available).
Deep learning algorithms are typically less complex than machine learning algorithms and can be run on standard computers, whereas machine learning systems require far more powerful hardware and resources.
Because of the increased demand for power, the use of graphical processing units has increased. GPUs are useful because of their high bandwidth memory and their ability to hide memory transfer latency (delays) due to thread parallelism (the ability of many operations to run efficiently at the same time).
To solve a given problem, the traditional ML model divides the problem into sub-parts and produces the final result after each part is solved. A deep learning model's problem-solving approach differs from that of a traditional ML model in that it takes input for a given problem and produces the end result. As a result, it adheres to the end-to-end approach.
Machine learning typically necessitates structured data and employs traditional algorithms such as linear regression. Deep learning makes use of neural networks and is designed to handle large amounts of unstructured data.
It is simple to interpret the outcome of a given problem. We can easily interpret the results of machine learning when we work with it, which means we can understand why this result occurred and what the process was. The solution to a given problem is extremely difficult to interpret.
For example, when working with the deep learning model, we may obtain a better result than the machine learning model for a given problem, but we cannot determine why this particular outcome occurred and the reasoning behind it.
Machine learning is already in use in places such as your inbox, bank, and doctor's office. Deep learning technology enables more complex and self-sufficient programs, such as self-driving cars or surgical robots.
Machine learning models are best suited for tackling simple to moderately complex problems. Deep learning models are ideal for tackling complex problems.
Deep learning can be defined as machine learning with more capabilities and a different working approach. ML and DL are two subsets of artificial intelligence's majesty. They both have undeniable value in modern human life, but they do not work in tandem to solve the same problems.
And choosing one of them to solve a specific problem is determined by the amount of data and the complexity of the problem. The primary distinction between machine and deep learning is in how data is delivered to the system.
While Machine Learning relies on how it was trained by humans, Deep Learning relies on artificial neural connections and does not require human intervention.