Deep learning and machine learning seem to be interchangeable terms in the artificial intelligence industry. That, however, is not the case. In fact, anyone interested in learning more about AI should start by learning the terminologies and differentiating them.
The real kicker is that it’s not as tough as some of the articles on the subject lead you to believe. Machine learning and deep learning are two artificial intelligence subcategories that have gotten a lot of interest in recent years.
In this article you’ll find the essential information about:
- What is machine learning
- What is deep learning
- Types of deep learning algorithms
- Deep learning vs machine learning comparison
- The future of DL & ML
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What is Machine Learning?
Machine learning is a type of data analysis that streamlines the creation of analytical models. It’s a field of artificial intelligence predicated on the concept that computers can learn from data, understand trends, and make choices with little or no human involvement.
Machine learning is the technology that underpins many of the applications and services we utilize today, including:
- Netflix, YouTube, and Spotify’s recommendation systems
- Google search engines
- Facebook and Twitter’s news feed
- Siri and Alexa’s voice assistants
Machine learning was inspired by pattern recognition and the idea that computers may learn without being trained to do certain tasks; artificial intelligence scientists sought to test if machines could learn from data.
The recurrent component of machine learning is essential because machine learning models may adjust autonomously as they are exposed to fresh data. They use prior calculations to generate consistent, consistent judgments and outcomes.
Machine learning algorithms use a number of approaches to make judgments based on vast quantities of complicated data. With particular inputs provided to the machine, these algorithms accomplish the process of learning from data. It’s critical to comprehend how these algorithms function so that we can learn how to use them effectively.
The four types of machine learning algorithms are:
- Supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Below is a brief discussion of each algorithm.
The machine is trained through examples in Supervised Learning. The controller gives the machine learning algorithm a piece of predefined information including the intended results. Once the dataset has been identified, the algorithm must figure out a way on how to get to those inputs and outputs.
The algorithm finds correlations in data, learns from observations, and produces projections while the controller is aware of the specific answers to the given problem. He/She will then correct the algorithm’s forecasts, and the process repeats until the algorithm reaches a high degree of accuracy or performance.
There are three main tasks under supervised learning:
- Classification – the machine learning program must make a judgment based on the observed values and decide which group new observations fall under. One good example is when classifying emails as malicious or not, the computer must consider previously observed data before classifying the emails.
- Regression – the machine learning algorithm must measure and comprehend variable correlations. Regression analysis relies on one dependent variable and a set of other dynamic variables, making it ideal for prognosis.
- Forecasting – practice of generating predictions based on historical and current data, and it is often used to analyze trends.
Semi-supervised learning is comparable to supervised learning, except it incorporates both marked and unmarked datasets. Labeled data is information that includes relevant tags so that the machine learning algorithm can comprehend it, whereas unlabeled data does not have those tags. Machine learning systems can learn to identify unlabeled data using this approach.
The machine learning program looks for patterns or relationships in the given data. No available assistance is provided. Instead, the machine analyzes accessible data to find connections and connections. The machine learning algorithm is left to understand huge datasets and respond to them in an unsupervised learning process.
The algorithm attempts to organize the data in such a way that its structure can be described. This might entail organizing the data into clusters or rearranging it in a more logical manner. As it evaluates additional data, its capacity to make judgments based on that data increases and refines.
Unsupervised Learning features the following procedures:
Reinforcement learning is concerned with structured learning processes in which a machine learning algorithm is given a series of procedures, variables, and outcomes to work with.
The machine learning algorithm intends to uncover several alternatives and solutions after establishing the criteria, reviewing and analyzing each outcome to decide which is the best.
Reinforcement learning instructs the system to learn via experimentation. It takes what it has learned in the past and adapts its strategy to the environment in order to get the greatest potential output.
What is Deep Learning?
Deep reinforcement learning or deep learning is a subset of a larger family of machine learning techniques based on representation learning and artificial neural networks.
There are three types of learning:
It is also defined as a subclass of machine learning in which algorithms are constructed and work similarly to machine learning. However, several layers of these algorithms offer a distinct analysis of the data it draws on.
Artificial neural networks are a type of network of algorithms whose functionality is inspired to mimic the behavior of the neural networks found in the human brain. Since neural networks include numerous (deep) layers that permit learning, the subject was referred to as Deep Learning.
Deep learning is seen as the future milestone in machine learning. As a matter of fact, you may have unconsciously experienced the effects of a comprehensive deep learning program.
Siri and Google Assistant in their most basic versions are good examples of programmed machine learning since they are efficient in their predefined scope. Google’s deep mind, on the other hand, is an excellent demonstration of deep learning. In its most basic form, deep learning refers to a computer that learns on its own through a variety of trial and error approaches.
Chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project Andrew Ng described deep learning through analogy:
“I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel. The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”
Some of the most common examples of applications of Deep Learning are the following:
- Driverless Vehicles
- Virtual Assistants (e.g. Siri, Alexa, Cortana)
- Facial Recognition
- Tailored Experiences
- Aerospace and Defense
We’re always moving forward with technology, which means that deep learning today has a higher recognition performance than it has ever had before. As a result, gadgets are consistently able to satisfy the demands of their users. It’s all about achieving safety requirements and serving a function on the road with items like autonomous vehicles.
Types of Deep Learning Algorithms
Several algorithms are used in deep learning models. While no network is flawless, certain algorithms are more appropriate for specific applications than others. To select the best, it’s necessary to have a thorough grasp of all main algorithms. Here are two of the major algorithms you should know about:
Convolutional Neural Networks
A convolutional neural network is an ANN or artificial neural network that is specially intended to analyze pixel information and is utilized in image recognition and processing.
CNNs are excellent artificial intelligence (AI) systems, image processing that utilizes deep learning to go through both generative and descriptive processes, frequently including computer vision, which includes recommendation systems, image and video recognition, and natural language processing.
The excellent efficiency of convolutional neural networks with pictures, voice, or audio signal inputs sets them apart from other neural networks. CNNs are known for their three main types of layers:
- Convolutional layer
- Pooling layer
- Fully-connected (FC) layer
A CNN utilizes a technology similar to a multilayer perceptron that is optimized for low processing needs. An input layer, an output layer, and a hidden layer with numerous convolutional layers, pooling layers, fully-connected layers, and normalizing layers make up a CNN’s layers.
The elimination of restrictions and improvements in image processing performance results in a system that is significantly more efficient and easier to train for image analysis and natural language processing.
Recurrent Neural Networks
Recurrent neural networks are artificial neural networks that are frequently utilized in natural language processing and voice recognition. Recurrent neural networks identify the progressive features of the input and utilize patterns to forecast the next most probable outcome.
Deep learning and the creation of models that replicate neuron activity in the human brain both utilize RNNs. They’re particularly useful in situations when the context is crucial to projecting a result, and they’re different from other artificial neural networks since they leverage feedback loops to analyze a sequence of information that influences the final result. This result is frequently referred to as memory.
Language models in which predicting the next word in a phrase is reliant on the information that comes before it is common RNN applications. RNN-based writing is a type of algorithmic creativity. The AI’s knowledge of syntax and semantics gained from its training set enables this imitation of human inventiveness.
The fundamental reason why recurrent neural networks are more interesting is they allow people to work with sequences of vectors in the input, output, or both. Below are the common types of RNNs:
Deep Learning and Machine Learning Comparison
Machine learning algorithms are sometimes characterized as deep learning algorithms. As a result, it may be more useful to consider what makes deep learning unique within the discipline of machine learning. The answer is the Artificial Neural Network or ANN algorithm structure, which requires less human involvement and requires more data.
As previously mentioned, deep learning is a subset of a larger family of machine learning techniques based on representation learning and artificial neural networks. There are three types of learning: supervised, semi-supervised, and unsupervised.
It is also defined as a subclass of machine learning in which algorithms are constructed and work similarly to machine learning. Machine learning, once again, is a type of data analysis that streamlines the creation of analytical models.
It’s a field of artificial intelligence predicated on the concept that computers can learn from data, understand trends, and make choices with little or no human involvement.
Deep Learning vs. Machine Learning Comparison Chart
Machine learning is a subfield of Artificial Intelligence that allows a system to learn and grow from its experiences without having to be coded to that extent. Data is used by Machine Learning to learn and discover correct outcomes. Machine learning is necessary for the creation of a computer software that can access data and learn from it.
Deep Learning is a subtype of Machine Learning in which a recurrent neural network and an artificial neural network are linked. The algorithms are generated in the same way as ML algorithms are, however, there are many more tiers of algorithms.
The artificial neural network refers to all of the algorithm’s networks put collectively. In much simple terminology, it mimics the human brain by connecting all of the neural networks in the brain, which is the notion of deep learning. It uses algorithms and a technique to tackle all types of complicated issues.
The Future of DL & ML
The latest technological breakthroughs were unimaginable. We could never have envisioned a humanistic computer, self-driving cars, or improved medical treatments. However, thanks to the power of deep learning, these have now become a part of our daily lives and will continue to do so in the next generations to come.
The future of Deep Learning is seen through these applications according to towards data science:
Machine learning technologies are becoming increasingly common in our daily lives as they continue to integrate improvements into organizations’ essential operations. By 2027, the worldwide machine learning market is expected to increase from $8.43 billion to $117.19 billion. Because machine learning algorithms have the ability to produce more reliable predictions and business judgments, several firms have already started utilizing them. Machine learning startups will receive $3.1 billion in investment in 2020. In fact, machine learning has the potential to revolutionize a variety of sectors.
Here are some of the most relevant machine learning applications today:
Machine learning and deep learning will have a long-term impact on our lives, and their abilities will change nearly every sector. Dangerous occupations, such as space travel or labor in extreme conditions, might be completely replaced by machines.
Simultaneously, people will be looking towards utilizing AI to provide valuable and fresh entertainment experiences that seem out of this world. Furthermore, in order to assist machine learning and deep learning reach their optimum results, it will need the ongoing efforts of brilliant professionals.
While each profession will have its own unique requirements in this area, there are a few major career pathways that are presently in high demand such as Machine Learning Engineers, Computer Vision Specialist, and Data Scientists.