Machine Learning and Deep Learning

Milica author

Milica Vujasin

Content Writer

⏱ Reading Time: 5 minutes

Everything started with AI. Yes, AI came first, but 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 companies have started to implement 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. Machine learning main focus is on the development of computer programs. And all that so programs could 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. Machine Learning gained popularity in the 1990s by borrowing AI’s core concepts. 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 to conclude machine learning, its goal is to leave human intervention out. 

Some of the machine learning algorithms are:

  1. Decision trees
  2. Naive Bayes
  3. Random forest
  4. Support vector machine
  5. K-nearest neighbor
  6. K-means clustering
  7. Gaussian mixture model

Some of the machine learning applications are:

  1. Image Recognition
  2. Speech Recognition
  3. Email filtering
  4. Medical Diagnosis
  5. Learning Associations
  6. Recommendation system
  7. Prediction
  8. Information Extraction (IE)
  9. Statistical Arbitrage
  10. Classification

Key industries where Machine Learning is implemented: financial services, marketing & sales, health care and more. 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. And when people use the term Deep Learning, they are actually referring to Deep Artificial Neural Networks, which is a technical term. 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.

So in general, a neural network is a technology built to simulate the activity of the human brain. Neural networks were developed in the 1950s. Did you know that traditional neural networks only contain 2-3 hidden layers? And deep networks can have as many as 150. Convolutional neural networks (CNN or ConvNet) are one of the most popular types of deep neural networks.

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

Deep Learning has implemented many methods and each of them is specific one. They can rely on many variables, such as what kind of task you want to solve your data. And since it all depends on variables, these methods are designed to choose which one can be your best problem solver:

  1. Convolutional Neural Network,
  2. Recurrent Neural Network,
  3. Generative Adversarial network
  4. Long short-term memory,
  5. Auto-Encoders,
  6. Denoising autoencoder,
  7. Stacked autoencoder,
  8. Sparse autoencoder,
  9. 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. That simply means it can also gain much more information from large data.

Math Works points out:

“Another key difference is 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.”

Deep Learning vs Machine Learning

Photo 1. Deep Learning vs Machine Learning

Artificial Intelligence and Learning Algorithms

According to the John McCarthy, Artificial Intelligence (AI) is:

The science and engineering of making intelligent machines, especially intelligent computer programs.

So, can you imagine it as vast field of studies with numerous branches making computer intelligent?

field where the machine is programmed 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. Simply, AI can free individuals from repetitive tasks thanks to comprehensive algorithms, which computer can do much more quickly and effectively 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.

Artificial Intelligence examples

Photo 2. Artificial Intelligence

Some of Artificial intelligence significant features are:

  1. Speech recognition
  2. Object detection
  3. Solving problems and learning from a given set of the data set
  4. Plan an approach for the future assignments

Examples of AI

Great examples of AI are movies Ex Machina, Avengers or Iron Man. It is a kind of system which understands all human communications and tells human nature and even gets irritated in points. 

AI specialization:

AI includes the following areas of specialization:

  1. games playing
  2. expert systems
  3. natural language
  4. neural networks
  5. robotics

Conclusion

AI has two basic components, which are Machine Learning and Deep Learning. 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 method that teaches computers to natural things people do: learn by example.

 

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Machine Learning and Deep Learning was last modified: September 10th, 2019 by student@paldesk.com
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