How Deep Learning is Different from Machine Learning?


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Artificial intelligence (AI) is the much needed and expected technology of this era. It is the ability of machines to learn cognitive functions like learning and problem-solving. There are other forms of AI that include emotional and social intelligence. AI that we use now is narrow as it can only perform certain actions. Both Deep learning and machine learning are used in the creation of AI. The key difference between them is the structure of the algorithm that is used. An algorithm is a set of instructions that the machines can use to execute certain tasks. Before discussing the difference between deep learning and machine learning, let us understand each of them clearly.

Table of Contents

Comparison Machine Learning vs Deep Learning

SINOComparison MetricsMachine LearningDeep Learning
1.Algorithm structureSimpleComplex or multi-layered
2.Data requirementLowHigh
3.Hardware requirementLowHigh
4.Feature learningManual input needIt learns by itself
5.Solving problemsSimple decisionsComplex decisions
6.Execution timeHighLow
7.InterpretationEasyDifficult

Machine learning

Machine learning is a subset of AI which relies on patterns and interference instead of explicit instructions. Machine learning algorithms are built using samples and are designed to make decisions based on the sample training data. These algorithms are mathematical models related to computational statistics. Machine learning performs predictive analysis using data mining. Data mining and machine learning are similar in the way they operate but their purpose differs. Data mining is used to discover missing data but machine learning is used to make predictions. Machine learning also shows similarity to statistics and optimization.

Types of Machine learning

Machine learning is classified into several types. They have supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, and anomaly detection.

In supervised learning both the input and output are fed as training data. The input can either be partial or complete.

In unsupervised learning, only the inputs are fed. Unsupervised learning the input data is used to find patterns and groups or clusters. In reinforcement learning, the software agents are made to take action for achieving a required result. Reinforcement learning is used in games, simulations, statistics, etc.

Self-learning doesn’t require external devices and driven by the interaction between emotion and cognition.

Feature learning saves the data that is fed and transforms it to make predictions better.

In sparse dictionary learning, the data is presented in linear combinations called the sparse matrix. It works on a theory that clean data can be represented by a sparse dictionary but incorrect data couldn’t be.

Anomaly detection also is known as outlier detection identifies the items that don’t match with the others.

  • Models – The main objective of machine learning is to learn from past data and perform an operation on the new data based on its training data. Machine learning involves making models out of the training data. There are various types of models that are used. They are artificial neural networks, decision trees, support vector machines, Bayesian networks, and Genetic algorithms.
  • Artificial neural networks (ANN) – Artificial neural networks mimic biological neural networks to learn to improve based on the training data. ANN is based on connected units called artificial neurons. These neurons are organized in layers to perform different operations on the input. It is used for several purposes including speech recognition, video games, filtering social networks, etc.
  • Decision trees – It uses a tree-like model of decision and possible outcomes. The algorithm uses only control statements. This model is used in operation analysis.
  • Support vector machines – It is supervised learning models that analyze data for classification and regression analysis. It can perform binary linear classification and non-linear classification using kernal trick.
  • Bayesian network – It is a probabilistic graphical model that represents a set of variables and their conditions using a directed acyclic graph (DAG). It is used for predicting the cause of an event by prediction.
  • Genetic algorithm – It is inspired by the process of natural selection and is used for optimization and search problems.
  • Limitations of machine learning – Machine learning has helped several fields in improving efficiency but it has failed to meet the expectations. The reasons for the failure are inadequate data, limited access to data, privacy concerns, etc.

Deep learning

Deep learning also called deep structured or hierarchical learning is a broad learning algorithm based on artificial neural networks. Deep learning has several layers in the model and each layer impacts the output. It has a credit assignment path (CAP) in which the input goes through a chain of transformations. This is achieved by creating a layer by layer model.

  • Deep neural networks (DNN) – DNN is derived from ANN and has several layers between the input and output. The layers are arranged either in a linear or non-linear fashion. DNN creates compositional layers in which each layer processes the input processed by the previous layer.
  • Recurrent neural networks (RNN) – In RNN the data can flow in any direction and are used in language modeling.
  • Convolutional deep neural networks (CNN) – CNN is used for computer vision and automatic speech recognition.
  • Limitations of deep learning – If not trained properly deep learning doesn’t provide good results. Two main problems that can happen with deep learning are computation time and overfitting.

Difference between machine learning and deep learning

We have learned some basics of machine learning and deep learning. Now let us see the differences between them.

  1. Structure of the algorithm – As mentioned before machine learning algorithm is a simple mathematical model to learn from examples provided and make predictions. But a deep learning algorithm has excess layers between the input and output. These layers process the input and perform a chain of operations.
  2. Data requirement – Machine learning uses a simple algorithm and it can perform well with a small number of samples or training dataDeep learning, on the other hand, needs a large number of data to learn and perform the required tasks.
  3. Hardware requirement – Machine learning doesn’t require a powerful system or server to operate. Whereas deep learning requires high configurations of systems and servers
  4. Feature learning – In machine learning, sample features are to be identified manually and fed into the system. Deep learning algorithms automatically analyze the features based on the input.
  5. Solving problems – Machine learning is suitable for performing simple operations. Large operations should be divided into parts to operate. Deep learning can work on complex tasks without splitting up the task.
  6. Time taken to execute an operation – While learning from sample data machine learning performs faster than deep learning. While execution deep learning performs faster than machine learning.
  7. Interpretation – In machine learning the rules are simple and it is easy to understand or analyze the processed output. Deep learning, on the other hand, is complex and the processed output is difficult to analyze or understand. This is because we are feeding only the input to the algorithm. We can’t guess why it has performed the task the way it has.

Conclusion

To conclude you may use machine learning or deep learning depending on your requirement. You may use machine learning to automate some processes in your business or to analyze data to leverage your business. You can use machine learning to stay ahead of your competition. You can use deep learning when you have a large database and the required output can’t be met by machine learning. You need to spend a lot of money on the hardware and resources if you choose to use deep learning.

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