Deep Learning vs Machine Learning – Demystified in Simple Words

Deep Learning and Machine Learning are the two most trending technologies in the world today. These technologies are often used interchangeably. While Deep Learning is the subset of machine learning, many people get confused between these two terminologies. So, for clearing this confusion today, we came up with our new article – Deep Learning vs Machine learning. This article consists of the feature-wise difference between both. Also, we will discuss applications, future trends, and where

Introduction to Deep Learning and Machine Learning

1. What is Machine Learning?

Machine Learning is the scientific study of statistical models and algorithms that computer systems use to perform a task without explicit instructions. Machine Learning is a comprehensive field that involves various functionalities of machine learning operations like clustering, classification and development of predictive models.

Basically, Machine Learning allows computers to learn without an explicit need for programming.

In general programming scenarios, you have to provide instructions to the computer for it to provide you with the output. However, with the help of machine learning algorithms, you can train your computer to provide you with the output with the need to give instructions. A machine learning algorithm is able to do so with the help of data. Using the data fed to the system, a machine learning algorithm is trained to provide output to the users. There are three main types of machine learning algorithms –

1.1 Supervised Learning

In a supervised learning algorithm, the input data is labeled such that the data is organized. The computer is able to follow the examples of input-output pairs and train the model to fit the data with good accuracy. Some of the supervised learning algorithms are –

• Linear & Multivariate Regression
• Logistic Regression
• Naive Bayes
• Decision Trees
• K-nearest neighbour
• Linear Discriminant Analysis
• Artificial Neural Networks

1.2 Unsupervised Learning

In Unsupervised Learning, the data is not labeled or categorized. In Unsupervised learning, the data is able to organize itself after it follows a certain pattern in the way the data is distributed. Unsupervised learning algorithms are complex and are currently under research. Some of the unsupervised learning algorithms are –

• Clustering Analysis
• Anomaly Detection
• Hierarchical Clustering
• Principal Component Analysis

1.3. Reinforcement Machine Learning Algorithms

We use these algorithms to choose an action. Also, we can see that it is based on each data point. Moreover, after some time the algorithm changes its strategy to learn better. Also, achieve the best reward.

Machine Learning is used in various industries that require future prediction, identification of patterns and autonomous decision making. It is widely used in healthcare, finance, banking, manufacturing and transportation sectors.

2. What is Deep Learning?

• The Input Layer

• The Hidden Layer

• The Output Layer

In the above example, the layers are present in the form of input layer, that takes the input data. The hidden layer, which performs various computation on the input data and the output layer, which in the above visualization is binary. It is to be noted that a neural network can have multiple hidden layers.

These neural networks are used to predict the output as well as perform classification on the data. The standard notion is that the neural network learns the pattern of data, then performs predictions that fall in the same line as the pre-specified pattern.

Deep Learning vs Machine Learning

We use a machine algorithm to parse data, learn from that data. And make informed decisions based on what it has learned. Basically, deep learning is used in layers to create an artificial “neural network”. That can learn and make intelligent decisions on its own.

Let’s understand the comparison of deep learning vs machine learning through their features –

1. Data dependencies

Performance is the main key difference between both algorithms. Although, when the data is small, deep learning algorithms don’t perform well. This is the only reason Deep Learning algorithms need a large amount of data to understand it perfectly.

But, we can see the use of algorithms with their handcrafted rules prevail in this scenario. Above image summarizes this fact.

2. Hardware dependencies

Generally, we use traditional algorithm to solve problems. Although, it needs to break a problem into different parts. Further, solve them individually. Moreover, to get a result, combine them all.

For Example – 

You have a task of multiple object detection. Although, in this task we have to identify what is the object and where is it present in the image. Further, in a machine learning approach, we have to divide the problem into two steps:

• Object detection
• Object recognition

First, we use grabcut algorithm to skim through the image and find all the possible objects. Then of all the recognized objects, you would then use object recognition algorithm like SVM with HOG to recognize relevant objects.

5. Execution time

6. Interpretability

We have interpretability as a factor for comparison of both learning techniques. Although, deep learning is still thought 10 times before its use in industry.

You must check the master comparison of AI vs Data Science vs ML vs Deep Learning

Where is Machine Learning and Deep Learning Being Applied?

1. Computer Vision

We use this both for different applications like vehicle number plate identification and facial recognition.