Machine Learning and Deep Learning
Everything started with AI. But even though AI came first, both Deep Learning and Machine Learning are on the boom from quite some time, and it is predicted to stay like that for at least a decade from now. Nowadays industries have started to deploy Deep and Machine Learning algorithms as it generates them more revenue.
And consequently, they are educating their employees to learn this skill, pass them to others and contribute to their companies. To possess deep learning and machine learning skills will most likely play a significant role in the coming years.
What is Machine Learning?
According to Wikipedia,
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence.
Or simply, we can say it is an application of AI that is using data you have to make predictions. It focuses on the development of computer programs that can access data and use it to learn for themselves.
We can go back to the beginning of the Deep Learning process. The term was first coined by Arthur Samuel in 1959 when interest in AI was beginning to grow. Borrowing the core ideas of AI, Machine Learning gained prominence in the 1990s. And it all began with data and observations to notice patterns in data so we can learn from them.
With all that data we will be able to transform things we learned into better decisions in the future that will have a great impact. So, the main goal is to leave human intervention out and to allow computers to learn automatically.
Some of the machine learning algorithms are:
- Decision trees,
- Naive Bayes,
- Random forest
- Support vector machine,
- K-nearest neighbor,
- K-means clustering,
- Gaussian mixture model, etc.
Some of the machine learning applications are:
- Image Recognition
- Speech Recognition
- Email filtering
- Medical Diagnosis
- Learning Associations
- Recommendation system
- Information Extraction (IE)
- Statistical Arbitrage
Key industries where Machine Learning is implemented: marketing & sales, health care, financial services and etc. It is expected that in a couple of decades the mechanical, repetitive tasks from all over different industries will be over.
What is Deep Learning?
Deep learning is a subset of machine learning. But when people use the term Deep Learning, they are referring to Deep Artificial Neural Networks that is a technical term. And neural networks refer to the number of layers in a neural network.
Techopedia explains Deep Neural Network:
A deep neural network is a neural network with a certain level of complexity, a neural network with more than two layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways.
A neural network, in general, is a technology built to simulate the activity of the human brain – specifically, pattern recognition and the passage of input through various layers of simulated neural connections.
Neural networks were developed in the 1950s. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150. One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet).
What is important to realize is that Deep Learning is not only a subset of machine learning but also one of the many approaches. Some may include clustering, decision tree learning, reinforcement learning, and others.
Deep learning methods
There are a lot of methods that Deep Learning implemented and each of them is a specific one. They can depend on many factors, such as what type of task you would want to solve your data. And since it all depends on factors, these methods are designed to choose which one can be your best problem solver:
- Convolutional Neural Network,
- Recurrent Neural Network,
- Generative Adversarial network
- Long short-term memory,
- Denoising autoencoder,
- Stacked autoencoder,
- Sparse autoencoder,
- Variational autoencoder.
Deep Learning vs Machine Learning
The easiest way to understand these two learning is the fact that deep learning is machine learning. To be more specific, it’s the next evolution of machine learning – it’s how the machine will be able to make decisions without a program telling them so.
But some researches show that deep learning has more significant success at this moment. It is said the main reason is that DNN can have a huge capacity to store information. Therefore, if it can have a huge storage capacity that means it can also gain much more information from large data.
Another key difference is that deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction and modeling steps are automatic.
Photo 1. Deep Learning vs Machine Learning
Artificial Intelligence and Learning Algorithms
According to the father of Artificial Intelligence, John McCarthy, Artificial Intelligence (AI) is
The science and engineering of making intelligent machines, especially intelligent computer programs.
So, can you imagine it as a huge research field with numerous branches that are making a computer intelligent?
A field where you program the machine to become intelligent. The main benefit of AI would be the fact it can replicate human actions and decisions without human shortcomings, such as emotion. Thanks to detailed algorithms, AI can free people from repetitive tasks which computer can do much faster and more efficiently than human minds.
Related: How to Make Your Chatbot More Human?
Statistics say that 80% of B2B marketing executives predict AI will revolutionize their industry by 2020, while 64% of B2B marketers consider AI valuable for their sales and marketing strategy. So, if you want to improve and grow your business, surpass your competition, it’s the right time to consider implementing AI.
Photo 2. Artificial Intelligence
Some of the important features of Artificial intelligence are:
- Speech recognition
- Object detection
- Solving problems and learning from a given set of the data set
- Plan an approach for future tasks to be performed
Examples of AI
Ex Machina, Avengers or Iron Man movies are examples of AI. It is a kind of system which understands all human communications and tells human nature and even gets irritated in points. That is where the computing community calls a general Artificial Intelligence.
AI includes the following areas of specialization:
- games playing: teaching computers to play games against humans
- expert systems: teaching computers to make decisions in real-life situations
- natural language: teaching computers to understand natural language
- neural networks: computers that imitate intelligence by attempting to reproduce the types of physical connections that occur in animal brains
- robotics: teaching systems to see and hear and react to other sensory stimulations
Machine Learning and Deep Learning are basic components of AI, along with the Neural Network and Computer Vision. AI algorithms, that are also known as Machine Learning, learn differently than humans, they look at things differently.
They can see relationships and patterns that escape us and help us avoid everyday pattern and focus on other things. On the other side, Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
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