Machine learning and Deep Learning are major components of Artificial Intelligence. Technology companies are utilizing these technologies to offer the 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 the difference between Machine Learning vs Deep Learning.
Table of Contents
What is Machine Learning?
In machine learning, a 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. We suggest you also read this article on the applications of machine learning in manufacturing.
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.
Step-1: Define the Features
To identify and compare bicycles and cars, Firstly we need to identify their unique features. For example cars can have following unique features.
- Four Number of Tires
- 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 models. Each type of part you want to identify will have a different ML model. For example if you have 100 different categories of parts. You need to define a different model for each part. Depending on the type of available data, Here are the three types of Machine Learning algorithm.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning.
Step-3 Train Machine Learning Algorithm
ML algorithms required structured and labeled data for training. Training improves the accuracy of ML algorithms. 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 the ML model is working as expected. If results are not as per expectation, ML model is modified.
What is Deep Learning?
Deep learning is a branch of machine learning that is inspired by the functionality of the human brain called neurons. In deep learning artificial neural networks similar to human neurons are used for Learning and predictions. All information in Deep Learning algorithms is passed through these artificial neural networks. Ultimate Goal of deep learning is to make machines think like humans.
For example, self driving cars utilizes deep learning technology to predict and take decisions.
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 a lot of labeled data that is used to train the algorithm.
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 its own.
Difference between Machine Learning and Deep Learning
Deep learning is a subset of machine learning. In Machine Learning users 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.
Machine Learning vs Deep Learning
Here are the list of difference between Machine learning and deep Learning algorithms.
|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.|
|Applications||E-Mail Spam Detection, Fraud Detection, Stock Market Prediction||Amazon product suggestions, Facebook feeds, Google adds, Self Driving Car|
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