Machine Learning vs Deep Learning

Objective

In this article, we will study a comparison between Deep Learning and Machine Learning. We will also learn about them individually. We will also cover their differences on various points. Along with a Deep Learning and Machine Learning comparison, we will also study their future trends.

What Is Machine Learning?

Generally, to implement Artificial Intelligence, we use Machine Learning. We have several algorithms that are used for Machine Learning. For example:

• Find-S
• Decision trees
• Random forests
• Artificial Neural Networks

Generally, there are 3 types of learning algorithms:

1. Supervised Machine Learning Algorithms make predictions. Further, this algorithm searches for patterns within the value labels that were assigned to data points.

2. Unsupervised Machine Learning Algorithms: No labels are associated with data points. Also, these ML algorithms organize the data into a group of clusters. Moreover, it needs to describe its structure and make complex data look simple and organized for analysis.

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. After some time, the algorithm changes its strategy to learn better. 

What Is Deep Learning?

Machine Learning focuses only on solving real-world problems. It also takes a few ideas from Artificial Intelligence. Machine Learning goes through the Neural Networks that are designed to mimic human decision-making capabilities. ML tools and techniques are the two key narrow subsets that only focuses more on Deep Learning. We need to apply it to solve any problem that requires thought — human or artificial. Any Deep Neural Network will consist of three types of layers:

• The Input Layer
• The Hidden Layer
• The Output Layer

In the above example, the layers are present in the form of input layer, which takes the input data. The hidden layer, which performs various computations on the input data and the output layer 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. We can say Deep Learning is a sub-field of Machine Learning.

Comparison of Machine Learning and Deep Learning

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 DL 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. The above image summarizes this fact.

Hardware Dependencies

Generally, Deep Learning depends on high-end machines while traditional learning depends on low-end machines. Thus, Deep Learning requirement includes GPUs. That is an integral part of it’s working. They also do a large amount of matrix multiplication operations.

Feature Engineering

It’s a general process. In this, domain knowledge is put into the creation of feature extractors to reduce the complexity of the data and make patterns more visible to learn the algorithm working. Although, it’s very difficult to process. Hence, it’s time consuming and expertise.

Problem Solving Approach

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

For Example:

Let us suppose you have a task of multiple object detection. In this task, we have to identify what the object is and where is it present in the image. In a Machine Learning approach, we have to divide the problem into two steps:

• object detection
• object recognition

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

Execution Time

Usually, Deep Learning takes more time to train as compared to Machine Learning. The main reason is that there are so many parameters in a Deep Learning algorithm. Whereas Machine Learning takes much less time to train, ranging from a few seconds to a few hours.

Interpretability

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

Where Is ML and Deep Learning Being Applied?

a. Computer Vision:  We use this for different applications like vehicle number plate identification and facial recognition.

b. Information Retrieval: We use ML and DL for applications like search engines, both text search, and image search.

c. Marketing: We use this learning technique in automated email marketing and target identification.

d. Medical Diagnosis: It has a very wide usage in the medical field also. Applications like cancer identification and anomaly detection.

• Natural Language Processing
• For applications like sentiment analysis, photo tagging, online advertising, etc

Future Trends

• Nowadays, Machine Learning and data science are in trend. In companies, demand for them both is rapidly increasing. They're in demand particularly for companies who want to survive to integrate Machine Learning in their business.
• Deep Learning is discovered and proves to have the best techniques with state-of-the-art performances. Thus, Deep Learning is surprising us and will continue to do so in the near future.
• Recently, researchers are continuous in exploring Machine Learning and Deep Learning. In the past, researchers were limited to academia. But, nowadays, research in ML and DL is making their place in both industries and academia.

Conclusion

We have studied Deep Learning and Machine Learning and also looked at a comparison between the two. We have also looked at images for better representation and understanding. If you have any questions, feel free to ask in the comments section.