Machine Learning vs Deep Learning : What is the Difference

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Machine learning and Deep Learning are major components of Artificial Intelligence. All big companies are utilizing these technologies to offer best solution to their customers. But sometimes people use these words interchangeably. In reality “Deep Learning” is a subset of “Machine Learning”. In this article we will discuss about Machine Learning vs Deep Learning. We suggest you to real this article on the difference between machine learning and artificial intelligence.

What is Machine Learning?

In machine learning, computer program / machine can learn and improve by its own experience. ML is used to analyze, understand and identify a pattern in a data. It is a subset of Artificial intelligence that provides system capability to learn from his own experience.

In Machine learning we need to manually define the features to train machine learning model. It involves manual work which is prone to error.
Machine Learning Process

In Machine learning we need to manually define the features to train machine learning model. It involves manual work which is prone to error.

For example, if you want to build a ML program that can recognize and compare the car and bicycle. For that firstly we need to identify car and bike unique features. Based on these features ML algorithm will identify a pattern during training.

machine learning process involves collection of training data, defining and labeling features, training and testing ML algorithm and then predicting the things.
Machine Learning Workflow
Step-1: Define the Features

To identify and compare bicycle and car Firstly we need to identify their unique features. For example car can have following unique features.

  • Four Number of Tyres
  • Length more than 4 meter
  • Width more than 1.5 meter
  • Front Bumper
  • Rear Bumper
  • Side doors
Step-2: Create Machine Learning Model / Algorithm

Defined features are used to create ML model. Each type of part you want to identify will have different ML model. For example if you have 100 different category of parts. You need to define different model for each part. Depending on type of available data, following three types of ML algorithm are used.

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning.
Step-3 Train Machine Learning Algorithm

ML algorithm required structured and labeled data for training. Training improves the accuracy of ML algorithm. Availability of high quality training data ensures more accuracy in predictions.

Step-4 Test Machine Learning model

ML algorithm is first tested using known unlabeled data. It is required to ensure ML model is working as expected. If results are not as per expectation, ML model is modified.

What is Deep Learning?

Learning and predictions in Deep Learning are done in artificial neural networks. All information provided to DL algorithms is passed through these artificial neural networks.

Deep learning is a branch of machine learning that is inspired by the functionality of human brain called neurons.

In deep learning we utilizes artificial neuron network similar to human neurons for Learning and predictions. 

All information in Deep Learning algorithms is passed through these artificial neural networks. Ultimate Goal of deep learning is to capable machine to think like humans. 

Deep Learning Process

We do not need to manually define the feature to train deep learning algorithms. It creates unique features and models of their own. To train deep learning models we only need to provide lot of labeled data that is used to train the algorithm.

Learning and predictions in Deep Learning are done in artificial neural networks. All information provided to DL algorithms is passed through these artificial neural networks.

For example to train Dl model to identify car or bicycle. You need to upload lot of images of bicycle and cars and define then it’s car or bicycle. Deep learning algorithm will create the model and features by it’s own.

Machine Learning vs Deep Learning

Description Machine Learning Deep Learning
Definition In this, computer program / machine can learn and improve by its own experience. Inspired by structure and function of human brain. Learning and predictions is done in artificial neural networks.
Scope Broader Term Subset of Machine Learning
Labeled Features Features are defined manually. Therefore chances of error are high. Features are defined by deep learning algorithm itself.
Data Requirements Small and medium data sets are enough to train ML algorithms. Lots of labeled data is required to train deep learning algorithms.
Computing Requirements Can work on low-end machine Requires high computing power.
Training Time Few minutes to hours. Few weeks to months.
Accuracy Good Best
Development Cost Low High
Applications E-Mail Spam Detection, Fraud Detection, Stock Market Prediction Amazon product suggestions, Facebook feeds, Google adds, Self Driving Car

Conclusion

To sum up, Deep learning is a subset of machine learning. In Machine Learning user need to define the features of the data to be predicted. Whereas in deep learning data features are identified by deep learning algorithms itself. Therefore chances of error in deep learning is less. 

Got Questions?  We will be happy to help.

If you think we missed Something?  You can add to this article by sending message in the comment box. We will do our best to add it in this post.


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