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What Is The Difference Between Machine Learning And Deep Learning

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Machine learning and deep learning have a number of similarities. 

Like machine learning, deep learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. 

The two terms are sometimes used interchangeably because they can both be applied to pattern recognition problems. 

Deep learning simply applies to more complex scenarios where there are multiple layers involved in processing incoming data.

In general, machine learning refers to the development of computer programs that can access data and use it to learn for themselves. 

What Is The Difference Between Machine Learning And Deep Learning

The primary goal here is “learning” as opposed to programming by a human developer which requires explicit instructions on what should be done with given input data. 

So machine-learning algorithms need only specify their goals and how those goals will be achieved based on the data to which they are given access.

By contrast, deep learning is a specific approach to machine learning where multiple layers of computations are involved in processing input data to make sense of it. 

This is done by breaking down the complex data into simple representations or models that can be easily classified or labeled by humans. 

To do this, many deep-learning algorithms work in stages, progressively refining their results until they accurately reflect the patterns found in the data provided. 

Over time and with additional data, these programs become more accurate at what they do as errors get corrected and better models emerge from repeated processes of trial and error.

The two terms also differ in how they handle new information: Machine learning programs only learn new things if specifically programmed to do so, while deep learning looks for patterns in existing data to identify new information.

Both machine learning and deep learning are ways that computers can learn how to respond to stimuli like speech, text, images or videos without explicit programming. 

Both represent an advancement over earlier forms of computer automation which relied on pre-programmed rules coded by humans.

Instead, both use algorithms rather than predefined rules or programs to extract meaning from different types of data. 

These systems can be trained with vast amounts of data making them very good at recognizing patterns but not as good as humans when it comes to abstract reasoning (e.g., inferring the implications of a particular pattern).

The key difference between the two is that machine learning typically involves one or more shallow processing layers that gradually build up complex models from simple representations of individual elements, while deep learning involves many processing layers working in concert to extract increasingly abstract models of the data.

Machine-learning algorithms are typically applied to structured, numerical data found in spreadsheets or databases. 

Deep learning is used for raw unstructured information like images or videos. 

Another key difference between the two is how they handle new information: Machine learning programs only learn new things if specifically programmed to do so, while deep learning looks for patterns in existing data to identify new information.

While the concepts behind machine learning and deep learning are not necessarily difficult to grasp, fully understanding them requires a level of technical expertise that makes it challenging for non-experts to differentiate between the two.

What is the difference between machine learning and deep learning?

Machine Learning (ML) and Deep Learning (DL) are at the forefront of artificial intelligence technology today. 

Many people do not know how to differentiate these two concepts, but understanding their differences will be helpful in determining which is best suited for your needs. 

Machine Learning is one of the most popular forms of AI due to its ability to automate tasks without human intervention by leveraging big data algorithms. 

Deep Learning, on the other hand, works with neuron networks that learn through deep training with large sets of sample data. 

This type of AI requires specific programming techniques that may not be available to organizations lacking technical resources necessary for implementation. 

Since each has unique benefits, it is up to companies like yours to determine which is best for their needs.

Machine Learning

Machine Learning (ML) is one of the most popular forms of AI due to its ability to automate tasks without human intervention by leveraging big data algorithms. 

Machine learning uses statistical techniques to analyze large sets of data, create computer programs that can learn from this data, and then perform specific actions based on the analysis. 

This type of AI requires specific programming techniques that may not be available to organizations lacking technical resources necessary for implementation. 

The essential elements of machine learning include prediction, classification, clustering, pattern recognition and function fitting . 

In addition, they need a large amount of sample data with labeled attributes in order to perform effective training. 

Organizations interested in trying out machine learning can use open source or commercial machine learning software such as Weka and RapidMiner to perform their own analysis.

Deep Learning

Deep Learning (DL) works with neuron networks that learn through deep training with large sets of sample data. 

This is a type of AI that requires specific programming techniques that may not be available to organizations lacking technical resources necessary for implementation. 

The main difference between DL and machine learning is the different types of algorithms used in each process . 

Under traditional machine learning, there are two types: supervised and unsupervised learning. 

With supervised learning, the computer will predict an output after being trained on input samples where the correct answer is known by using labeled data points . 

Unsupervised learning creates models without human intervention or labeled inputs, so instead it groups say, images into clusters, and then it can identify features and classify new data points based on similar attributes to the ones it has learned about . 

On the other hand, deep learning uses a different type of algorithm called an artificial neural network (ANN). 

ANNs consist of layers or multiple interconnected neurons that work together to process information . 

It’s built around a feedback loop: as the ANN goes through each layer, it re-adjusts its internal structure until it converges on a solution for whatever problem is being modeled.

ML & DL Use Cases

The essential elements of machine learning include prediction, classification, clustering, pattern recognition and function fitting. 

For example:

- Fraud detection: Using data mining and pattern recognition algorithms to detect potential cases of fraud

- Classification: Identifying the type of an object based on its attributes. For example, classifying emails as spam or not spam or classifying cars as luxury cars or economy cars.

- Prediction : Forecasting an event based on historical data to predict future events for insurance underwriting purposes

- Clustering: Creating groups of similar data points based on their characteristics. This is used often in customer segmentation .

Organizations interested in trying out machine learning can use open source or commercial machine learning software such as Weka and RapidMiner to perform their own analysis. 

They offer a wide range of algorithms that allow you to perform different types of machine learning tasks without extensive development knowledge needed. 

Deep Learning is a subset of machine learning and also a subset of AI.

Deep Learning differs from Machine Learning in the way it employs artificial neural network algorithms to make sense of large, complex data sets. 

This advanced algorithm allows computers to learn without being explicitly programmed, opening up exciting new possibilities for businesses. 

Organizations will need to invest in staff with AI and machine learning expertise if they want to create their own deep learning models.

In short, Machine Learning requires an existing computer program that collects data from past experiences while, Deep Learning requires a Super Computer or other significant compute mechanism that can handle a vast amount of data and use this data to train a program how it should learn. 

In simple terms: 

1-Machine Learning is used by existing applications that have data available

2-Deep Learning is used to create new applications that are capable of learning without being programmed.