Introduction to deep learning vs machine learning vs AI
In a world full of technologies and advanced ways of living the dream life, we always get stunned every moment to see new surprises. Still, we often forget that technologies make them possible and leave us to whisper wow when we feel and love the surprises taking place around us.
These technologies are deep learning, machine learning, and artificial intelligence, making our lives easy living with amazing transformations and sensors. If you are still wondering what we are talking about, let me tell you that our present life will still be a dream without the technologies.
Thanks to these technologies for bringing the future so fast to our present days, making our life and world bright, and making easy communications happening, with reliable internet speed and better communication across the corner of the earth. One can proudly say we are not kilometers apart, but a video calls away from each other. That’s how technologies and our way of living changed over time.
When you finish reading this blog, you will already have some crystal clear ideas about deep learning, artificial intelligence, and machine learning. You will also get to know about deep learning vs machine learning vs AI.
Therefore, whenever you go to interviews or look for a referral when you face the walls of your path to overcome them. This blog will be your go-to guide. Just bookmark it for your future reference.
With these from the above, let’s dive deeper into the ocean of deep learning versus machine learning versus artificial intelligence and everything in detail about them.
What is Deep Learning?
Deep learning is the subset of machine learning in artificial intelligence that learns from unsupervised learning from unstructured data. In simple words, deep learning imitates human brains’ working in processing and creating effective patterns for better decision-making based on neural networks.
The best uses and applications of deep learning are detecting objects, speech recognitions, translating languages, and intelligent decision-making systems. It has the self-learning abilities that learn without human supervision from structured and unstructured data. The major applications of deep learning are fraud detection, spam mails, money laundering, etc.
How Deep Learning Works?
With digital transformation happening worldwide, one technology that evolved hand-in-hand is deep learning, bringing an explosion of data in all forms worldwide. And this data is what we call big data, and its sources are numerous, just like its name. They are from social media, search queries from the search engine by users, various e-commerce sites, etc.
These humongous data can quickly get accessible and shareable through a cloud computing system like Microsoft Azure. However, these data are generally in an unstructured format, and they are so vast that it could take a decade for humans to analyze and extract relevant information.
Some tech giants have already understood the incredible potential of this wealth of information and the use of artificial intelligence for the automation process to unravel them.
Three Types of Deep Learning Algorithms
- Generative Deep Learning Models
- Discriminative Deep Learning Models
- Hybrid Deep Learning Models
What is Machine Learning?
Machine learning is an advanced technology to analyze data by using analytical models through automation processes and advanced machine learning algorithms. Again, machine learning is a branch of artificial intelligence that learns from the data, identifies different patterns crucial for any business point of view, and makes predictions with minimal human intervention.
Just as its name, the outcomes it brings are similar to what humans can do, but the results are more accurate based on intelligent algorithms.
Some of the best-in-class machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, dimensional reduction, natural language processing, neural networks, etc.
How Does Machine Learning Work?
In machine learning, the learning process begins with observations. It can be a direct experience or instructions to look for the patterns for better decision-making. The prime objective is to allow computers or machines to learn automatically without human intervention.
But classic machine learning algorithms consider a text as the sequence of keywords based on semantic analysis that can imitate the human ability to understand the meaning of the whole text with 100% accuracy.
Machine learning can analyze the humongous data faster, giving more accurate results while identifying profitable opportunities and the risks in the entire process. It always requires additional time and resources to train correctly. When you combine machine learning with artificial intelligence and cognitive technologies, even the whole process becomes systemic, yielding 100% results in no time.
Three Types of Machine Learning Algorithms
This special algorithm consists of target variables (dependent variables) that get predicted from the given set of predictors (independent variables). ML engineers use this set of variables to generate a functional map input to the desired output. And the training process continues until the model achieves the desired level of accuracy on the training data.
Some of the best applications of supervised machine algorithms models are regression models, decision trees, random forests, KNN algorithms, logistic regression, etc.
In unsupervised learning algorithms, there is no target or outcome variable to predict or estimate. It uses clustering population techniques in different groups for segmenting customers into other groups for specific interventions. Best examples are – unsupervised learning, apriori algorithms, K means clustering, etc.
A reinforcement algorithm is a top-notch algorithm where the machine gets trained to make specific decisions based on various factors. It works on the trial and error principle and learns from it. It even learns from past experiences and takes the best possible knowledge to make accurate business decisions. Example: Markov Decision Process.
What is Artificial Intelligence?
Artificial intelligence, shortly AI, is a wide-ranging branch of computer science that focuses on building intelligent machines capable of performing top-notch tasks that typically require human intelligence to yield highly accurate results. Though it has multiple approaches, machine learning and deep learning create a paradigm shift in almost every field as it is the tech industry’s revolution.
AI jargons come with complications, not easily understandable by ordinary people, as it plays a crucial role in real-world applications. AI technology is disruptive, revolutionizing the way to make decisions the same way humans do. Therefore, all the people who use this technology as a part of daily life should understand these terms and jargon.
How Does Artificial Intelligence Work?
But before we begin there, let me ask you a simple question. Can machines think as humans do? If not, you live outside of our beautiful world. And if yes, how do they do it? Have you ever tried to explore? If you are yet to do it, let’s do it together. What do you say?
Many AI technologies are the most prominent buzzwords these days and revolutionary too. They are natural language processing, deep learning, and predictive analytics. These integral technologies have a common goal: they enable machines to understand human languages and learn from the outcome and experience. And make compelling predictions within the business goals and objectives.
While building your AI system, you need to be extra careful as it is the process of reverse engineering that imitates human traits and develops various capabilities inside machines to self-learn using advanced algorithms.
Therefore, to understand the whole process, the professionals must drive deep into different subdomains of artificial intelligence and gain insights into using advanced algorithms and understanding the fundamental logic and applications across multiple domains.
Types of Artificial Intelligence
Artificial Narrow Intelligence (ANI)
It is the most common artificial intelligence breed you will find in the market. These algorithms focus on solving only one problem at a time. And these algorithms can execute one task at a time effectively. These algorithms’ uses are very narrow, and outcomes are very high. You can easily find them on e-commerce sites while product recommendation to the eCommerce users or predicting the weather when you ask your intelligent assistance on your smart devices.
These algorithms are very close to human functioning and even surpass them in many parameters, but they are always limited to fewer functions.
Artificial General Intelligence (AGI)
This concept is very theoretical as it defines the human version of cognitive functions across multiple domains, including language processing, image processing, computational functioning, etc. Though technologies are getting smarter, we have started making AGI systems on intelligent devices that comprise multiple artificial narrow intelligence (ANI), working in tandem, communicating with each other to mimic human actions.
Artificial Super Intelligence (ASI)
This concept is a dream concept, and we are standing at the edge to enter into the science friction territory very soon. Thanks to AI, we live an extraordinary life of our future with so many advanced technologies as Artificial Super Intelligence (ASI) proves to be the logical progression from Artificial General Intelligence (AGI).
AI professionals use this concept for decision-making in things to connect well with the users with emotional relationships. Once we achieve various Artificial General Intelligence (AGI) feats, the AI system and algorithms will get enormous advancements into the realms that we have never dreamed of yet.
Purpose of Using AI
Artificial intelligence aims at strengthening human capacities. And make advanced decisions with far-reaching consequences. In other words, to reduce human efforts and get very accurate results that are beneficial to humanity by living a meaningful life.
By sharing different tools and algorithms over the past few years, there is enormous growth in the AI field. More and more ground-breaking tools are getting invented. The service we provide is in the company. We obtain as the customers will change to better in the coming years, leading happy customers and more profit to the business with seamless automation.
What is the difference between Deep Learning, Machine Learning, and Artificial Intelligence?
Machine Learning VS Deep Learning
Practically, deep learning is the subset of machine learning. In other words, deep learning is the particular function of machine learning algorithms. That is why most people are confused about both, as they are relatively similar, whereas the capabilities are different from each other.
The basic machine learning algorithms perform better than they can, but the deep learning algorithms are the advanced version. Therefore, people are often bewildered about deep learning vs machine learning pdf versions to clear the air.
If the outcomes are inaccurate in machine learning algorithms, then the experienced ML engineers have to supervise various steps to understand where it went wrong. Whereas with deep learning algorithms, the algorithms can make their predictions and learn from them, whether the outcomes are accurate or not based on the neural networks.
This example will clear deep learning vs machine learning and help you get a concrete understanding between two technologies, how similar they look, and how different they are in reality and their uses.
For example, you are very familiar with dark mode. How does your smart device do it for you by itself? It’s applying deep learning, which automatically gets programmed with the current time that when clock hour hands strike the six and minutes hand strikes twelve, the device will be in the night mode to protect your eyes.
But in the older version, your software is yet to get the updates, but there is a night mode option. You have to turn it on to get it. And it is how machine learning and artificial intelligence are similar and different from each other. Deep learning is an automated process, and you will see dark mode is off after sunrise for a better user experience.
Deep Learning VS Artificial Intelligence
Deep learning is a subgroup of machine learning and artificial intelligence. Algorithms that mimic the neurons in a human brain make the most use of deep neural networks. The machines and intelligent devices use different layers of algorithms to learn data differently.
The depth of the model represents the depth of layers in the models. The learning process is due to neural networks. The neural network is the architecture, whereas the layers are the stacks on each other from the top.
Artificial intelligence is the technique where machines behave like humans by replicating their behaviors. Artificial intelligence makes machines learn from the output of the results. These machines adjust their responses based on the new inputs while multitasking like humans while handling large amounts of data and recognizing patterns from the database.
Artificial Intelligence VS Machine Learning
Artificial Intelligence and machine learning are very much correlated to each other. The two technologies are the most trending technologies over the internet, creating intelligent machines that can think and perform accurately to human intelligence. There is a probability that humans may go wrong in some cases, but there are zero chances with devices unless it is wrong in the algorithms.
AI uses the more significant concept to create intelligent machines that simulate human thinking, capability, and behavior. In comparison, machine learning is the subset of AI that allows the device to learn from the data without explicitly programming.
The word derived from Artificial Intelligence means human-made and thinking power that machines possess to give very accurate results in no time.
Machine learning is the subgroup of artificial intelligence that enables machines to learn from the past structured, semi-structured and unstructured data to generate accurate results and give predictions based on the past data.
Machine learning works on specific models, and when you offer different data, it becomes irresponsible. The best uses are search engines, email spam filters, recommendation engines, Facebook auto friend tagging suggestions, etc.
Some Common Deep Learning Algorithms
The neural network is a structured network similar to the human brain and consists of artificial neurons. These networks stack to each other top-bottom into three layers: the input layer, the hidden layer, and the output layer based on the programming algorithms.
Data processed through each node as an input with the random weights, then multiply and add a bias. Finally, by applying the nonlinear functions, the neuron value is known.
Types of Neural Networks
- Convolutional Neural Networks
- Long Short Term Memory Neural Networks
- Recurrent Neural Networks
Generative Adversarial Network
Generative Adversarial Network, shortly (GAN) is a deep learning algorithm that creates new data instances resembling the training data. It has two components, a generator that generates the fake data and a discriminator that learns from the false information.
Its uselessness has massively increased over time, mostly in video game developers who use GANs to upscale low resolutions into 4K resolutions or even higher than it.
GAN helps to generate realistic and cartoon images, human faces and render 3D images. It learns from the fake generated data, and the discriminator can say it’s false data and helps to upgrade the model.
Multilayer Perceptron, shortly, MLP is an excellent algorithm to learn more about deep learning technology. It consists of both an input layer and an output layer interconnected with each other. The uniqueness of MLP is they have the same number of input and output layers, best for speech recognition, image recognition, and machine-translation software.
It takes the input data, the layers of neurons connect in the graphs so that the signal passes through one direction whole MLP weights the input data with the additional hidden layers, then it determines which nodes to fire. It then trains the models to understand the correlations between dependent and independent variables and the target data from the training data set.
Self Organizing Maps
Self Organizing Map, shortly SOM, uses data visualizations to reduce data dimensions using self-organizing artificial neural networks. Data visualization tools solve the problems that are arduous for humans to analyze and visualize high-dimensional data.
SOM initializes each node’s weight, chooses random data from the training data, consequences all the data that are most likely the input vector, and calls it the best matching unit (BMU). The SOM discovers the matching data in the neighborhood and yields the sample vector that gives more weight changes, and it repeats the same for N iterations until it gets the best matching unit.
Deep Belief Networks
Deep Belief Networks, shortly DBN, is a generative model consisting of multiple layers of latent variables, which has some binary values. Often we call it the hidden units. It interconnects within the coatings, and the best applications are image recognition, video recognition, and motion capture data.
Greedy learning algorithms to train DBN, using a layer-by-layer approach for learning the top-down, generative weights, run on Gibbs sampling on the top two hidden layers with the rest of the models. DBNs learn from the latent variables in every layer.
Some Common Machine Learning Algorithms
This method is convenient and best uses for continuous variables by establishing the relationship between independent and dependent variables by fitting the best line with the equation Y = a * X + b.
The best way to understand these equations and relations between them is to solve linear algebra problems by plotting the graphs and drawing the line based on the output and parameters. In this equation:
- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept
Linear Regression is of two types: simple linear regression and multiple linear regression.
Even the experienced professionals get confused by the name as it comes with a regression name. Still, it’s a classification model with discrete values 0 and 1, or yes or no, true or false based on the set of independent variables. Simply, it predicts the probability of an event’s occurrence by fitting the whole data to a logit function. Therefore, many call it logit regression as its output lies between 0 and 1.
Suppose your friend gives a puzzle to solve it, and you attend it. Either you will succeed with it, or else you will end up with a failure. There is no other way around. So here, the probability lies between zero and one, the discreet value. If the outcome is one, you solved it. If zero, then you have failed.
Decision trees are what we call supervised learning algorithms primarily used for classification problems; they work for categorical and continuous dependent variables. So the algorithms get split populations into two or more sets. Here machine learning engineers use tree-like models for better decision-making with a prime node and multiple child nodes as per the given data.
Here the classification trees concept gets used to classify the elements, whereas the regression trees represent the continuous variables. Decision Trees algorithms follow the idea of Classification and Regression Trees, shortly CART analysis.
Random forest works like trees where the data and model can get complicated using decision trees. The input data further subdivided into the group of decision trees where the output from all decision trees gets accepted. Random forests offer more accurate classifiers compared to decision tree algorithms.
Sometimes, decision trees fail to perform when there are humongous data, and solving them is crucial. Therefore, decision trees’ limitation gives rise to random forests where it uses multiple decision trees to solve complicated algorithms.
Support Vector Machine (SVM)
Support Vector Machine, shortly SVM, is a classification model. In these algorithms, we plot each data item in the n-dimensional space with the value of each featuring the particular coordinate. ML engineers use this method for both classification and regression techniques that yields accuracy in classification tasks.
This concept focuses on three points: data points, support vectors, and hyperplane. It can learn from both simple and complex problems that avoid overfitting. The prime purpose is to determine the best hyperplane (decision boundaries) that separate data points by giving the maximum possible margin within the support vectors.
Naive Bayes Concept
The naive Bayes classification technique is a supervised learning technique based on Bayes’ Theorem, assuming independence between predictors. In simple terms, a Naive Bayes model is an easy-to-build model and particularly useful for large datasets. And even Naive Bayes classifications are so robust that they can outperform highly sophisticated classification models.
Some Common Artificial Intelligence Algorithms
The artificial intelligence algorithms classify into three modes: Classification algorithms, regression algorithms, and clustering algorithms. Since we understand deep learning and machine learning and their algorithms, and as deep learning and machine learning combine to form AI, we already know about it. Let’s understand a few more algorithms that we are yet to discover.
Ridge and Lasso Regression
Ridge and Lasso Regression are excellent techniques to reduce the model complexity and prevent overfitting, which yields simple linear regression.
The cost function gets altered in ridge regression, adding penalties equivalent to the coefficients magnitude square. Therefore, ridge regression puts a constraint on the coefficients (w). Here the penalty (lambda) regularizes the coefficients such that if the coefficient takes the more significant value, the optimization functions get penalized.
In simple words, ridge regression shrinks the coefficients and reduces the model complexity and multicollinearity.
Lasso regression is a linear regression category that uses shrinkages to reduce the data values to shrunk towards the central point, primarily like the mean. It uses simple and sparse models with fewer parameters.
This sort of regularization (L1) can lead to zero coefficients. Therefore, some characteristics get neglected. Lasso regression doesn’t only help reduce overfitting, but it can also help you in element selections.
Multivariate regression is a supervised learning method that involves multiple data variables for analysis. It’s an extension of numerous regression with one dependent variable and multiple independent variables. Based on the number of independent variables, the output of the mathematical model gets predicted.
Multivariate regression techniques try to find a formula explaining how the variables respond simultaneously to change in other variables.
Hierarchical Clustering Algorithms
In other words, hierarchical cluster analytics is an algorithm that groups similar objects into clusters. The final point is a set of clusters, where each cluster is separate from the other in the form of a collection. And the elements from each group are very much similar to each other.
It operates, treating each observation as a separate cluster and repeatedly follows the two steps.
(i) identify the two sets closest to each other, and (ii) merge the two similar groups. The process continues until all collections get merged.
Fuzzy C-Means Algorithm
Fuzzy C-Means is a clustering method that allows one piece of data to belong to two or more clusters, and its best use is pattern recognition based on the minimization process. It is an iterative process between 0 and 1, where k is the number of iteration steps. In the FCM approach, the same doesn’t belong to well-defined clusters. You can place it in a middle way.
If the membership follows a smooth line to indicate every datum belongs to several clusters with different membership coefficient values.
A maximum likelihood estimate is an approach to estimating density for a data set by searching for probability distributions and their parameters. It is a general and practical approach to machine learning algorithms, although it requires till it accomplishes the training dataset.
This method is a practical and general approach, and ML engineers use density estimation techniques with missing data, such as clustering algorithms, Gaussian Mixture Model.
You got everything here that you always wanted to know about deep learning vs machine learning vs AI. These are the advanced technologies that rule the world of technology, teaching us to live a better way of living. These intelligent algorithms come with intelligent sensors. They get coded on with smart devices (computers and mobiles) to perform and give the end-users some incredible and mind-blowing performance.
You even learn about definitions of artificial intelligence, deep learning, and machine learning. And how each technology works, and can it be a game-changer in the upcoming days? You got to know about various algorithms under each technology and the purpose of using these technologies, such as deep learning, artificial intelligence, and machine learning.
With examples and some of the best use cases, you know about different deep learning categories, artificial intelligence, and machine learning. As neural networks from deep understanding, regression techniques from machine learning, and classification techniques from artificial intelligence, you get a walkthrough over everything to know each detail about these trending technologies.