Updated April 13, 2023
Difference Between Deep Learning and Machine Learning
Machine learning and Deep learning come under the same umbrella of Artificial Intelligence; machine learning has three different learning methods, i.e., Supervised, Unsupervised, and Reinforcement Learning. Whereas Deep Learning is the subset of machine learning due to which it poses few of the properties of machine learning but is different from it in aspects like the amount of data needed to train the model, Dependency on the hardware, Approach used to solve the problem, Execution Time, Featurization and Interpretation.
Head to Head (Infographics)
Below are the top 6 differences between Deep Learning vs Machine learning
Key Differences of Deep Learning and Machine Learning
Both machine learning and deep learning are a subset of artificial intelligence. Here are the main key differences between these two methods.
- In machine learning, the main focus is on improving the learning process of models based on their input data experience. In Machine learning, labeled or unlabelled data will first go through data engineering and featurization. The cleaner the data is fed, the good the model will be. In the case of deep learning, the focus is more towards making a model learn by itself, i.e., train and error method to reach to end solution.
- Machine learning is inclined towards atomization and predicting a regression or classification problem, like predicting whether the x customer will pay a loan based on n number of features. On the other hand, Deep learning tries to create a replica of the human mind in order to solve a specific problem, for example, by looking at pictures and recognizing which cat is the cat and which is Dog, etc.
- In machine learning, we deal with two types of problems supervised learning and unsupervised learning. In supervised input and output, data is labeled, on the other hand, in unsupervised learning, it is not. In the case of deep learning, it is a step further where the model approaches reinforcement learning. For every mistake made, there is a penalty and a reward for the right decision.
- In machine learning, we chose a suitable algorithm (sometimes multiple and then chose the best one for our model), define parameters and provide data, the machine learning algorithm will learn on train data, and upon verifying/evaluating with test data, the model will be deployed for a specific task. On the other hand, in Deep learning, we define a layer of the perceptron. A perceptron can be considered a neuron in the human mind. A neuron takes input through multiple dendrites, processes it (takes a small action/decision), and with axon terminals, sends output to the next neuron in the layer. In the same way, a perceptron has input nodes (coming from input data features or the previous layer of perceptron), an actuation function to make a small decision, and output nodes to send output to the next perceptron in the layer.
- The process to create a model from machine learning consists of providing features of input data, a select algorithm according to the problem, defining necessary parameters and hyper-parameters, training on the training set, and running optimization. Evaluate the model on test data. In the case of deep learning, the process is the same until providing input data with features. After this, we define the input and output layer of the model with the number of perceptrons in it. We choose the number of hidden layers required as per the complexity of the problem. We define Perceptron for each layer and, for each perception, the input, activation function, and output nodes. Once it is defined and then data is fed model will train by itself through trial and error.
- In Machine learning, the amount of data needed to create a model that is comparatively less. In the case of deep learning, the method is trial and error to learn the best possible outcome. So more the data is available for training, the stronger the model will be. In Machine learning, if we increase the amount of data too, but after a certain limit, the learning process will be stagnant. In the case of deep learning, the model keeps on learning, It’s the complexity of the problem, for a complex problem, more amount of data is required.
- For example, A machine learning model is used for providing recommendations for music streaming. Now for the model to make the decision about recommending songs/ albums/ artists, it will check the similar feature (music taste) and will recommend a similar playlist. For deep learning, the best example is automated text generation while searching for something on Google or writing a mail, A deep learning model automatically suggests possible outcomes based on previous experiences.
Let’s discuss the top comparison between Deep Learning and Machine learning
|Basis of Comparison||Deep Learning||Machine learning|
|Dependency on data||A comparatively large amount of data is needed, plus with the increase in input data performance increases||A sufficient amount of data can build a good model. But more than what is needed won’t improve performance as such.|
|Dependency on Hardware||High-end machines are a must.||Can work on small-end machines.|
|Approach used||In deep learning, the problem is solved in one go by using several layers of neurons.||A large problem is subdivided into several small tasks and in the end, are combined to build the ML model.|
|The time needed for Execution||More time is needed for execution because a number of neurons use different-2 parameters to build a model.||Comparatively, less execution time is needed in the case of ML.|
|Featurization||Deep learning learns from the data itself and does not need external intervention.||External intervention is necessary to provide the right input.|
|Interpretation||Hard to interpret the process of solving the problem. Because several neurons collectively solve the problem.||Easy to interpret the process in the machine learning model. It has logical reasoning behind it.|
We have discussed how the Machine learning model and deep learning models are different. We use Machine learning when data interpretation is simple (Not to complex) to provide automation in repetitive operations. We use a deep learning model when we have a very large amount of data or a problem is too complex to solve with machine learning. Deep learning needs more resources than that machine learning. It is expensive but more accurate.
This is a guide to Deep Learning vs Machine learning. Here we discuss the differences with infographics and comparison tables. You may also have a look at the following articles to learn more –