Machine Learning vs Deep Learning – How Are They Similar and Different
Artificial Intelligence is a trending topic these days. Machine learning and deep learning constitutes artificial intelligence. The Venn diagram mentioned below explains the relationship between machine learning and deep learning, which is as follows:
Machine learning is the art of getting computers to act as per the algorithms designed and programmed. It allows the computers to learn from the data already present. Many researchers think machine learning is the best way to make progress towards human-level AI. Machine learning includes various types of patterns like:
1. Supervised learning pattern
In this pattern, each example or illustration follows an algorithm which analyzes the training data and produces an inferred function.
2. Unsupervised learning pattern
In this learning pattern, a basic algorithm is used to draw inferences from a collection of unlabeled responses. The most common unsupervised learning method is cluster analysis which is used to identify hidden patterns of grouped data.
Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structure and function of the brain called artificial neural networks. All the value today of deep learning is through supervised learning or learning from labeled data and algorithms. Each algorithm in deep learning goes through the same process. It includes a hierarchy of nonlinear transformation of input and uses to create a statistical model as output.
Machine learning process is defined using the following steps which are mentioned below:
1. Identifies relevant data sets and prepares them for analysis.
2. Chooses the type of algorithm to use.
3. Builds an analytical model based on the algorithm used.
4. Trains the model on test data sets, revising it as needed.
5. Runs the model to generate test scores.
Difference between Machine Learning and Deep learning
1. Amount of data:
Machine learning works with various amounts of data and is useful for small amounts of data. Deep learning on the other hand works efficiently if the amount of data increases rapidly. The following diagram depicts the performance levels of machine learning and deep learning with regards to the amount of data.
2. Hardware Dependencies:
Deep learning algorithms are designed to heavily depend on high-end machines, contrary to the traditional machine learning algorithms. Deep learning algorithms do a large amount of matrix multiplication operations which requires a large amount of hardware support.
3. Feature Engineering:
Feature engineering is the process of putting domain knowledge into specified features to reduce the complexity of data and make patterns which are visible to the learning algorithms it works.
Example: Traditional machine learning patterns focus on pixels and other attributes needed for feature engineering process. Deep learning algorithms focus on high level features from data. It reduces the task of developing new features extractor of every new problem.
4. Problem Solving Approach
The traditional machine learning algorithms follow a standard procedure to solve the problem. It breaks the problem into parts, solves each one of them and then combines them to get the required result. Deep learning focuses on solving the problem from end to end instead of breaking them into divisions.
5. Execution time
Execution time is the amount of time required to train an algorithm. Deep learning requires a lot of time to train as it includes a lot of parameters which takes a longer time than usual. Machine learning algorithms comparatively require less execution time.
Interpretability is the major factor for comparison of machine learning and deep learning algorithms. The main reason is that deep learning is still thought twice before its usage in industry.
Applications of Machine learning and Deep learning:
1. Computer vision which is used for facial recognition and attendance mark through fingerprints or vehicle identification through number plate.
2. Information Retrieval from search engines like text search for image search.
3. Automated email marketing with specified target identification.
4. Medical diagnosis of cancer tumors or anomaly identification of any chronic disease.
5. Natural language processing for applications like photo tagging. The best example to explain this scenario is used in Facebook.
6. Online Advertising
1. With the increasing trend of using data science and machine learning in the industry, it will become important for each organization to inculcate machine learning in their businesses.
2. Deep learning is gaining more importance than machine learning. Deep learning is proving to be one of the best technique in state-of-art performances.
3. Machine learning and deep learning will prove beneficial in research and academics fields.
In this article, we saw an overview of machine learning and deep learning with illustrations and differences also focusing on future trends. Many of AI applications utilize machine learning algorithms primarily to drive self-service, increase agent productivity and workflows more reliable. Machine learning and deep learning algorithms include an exciting prospect for many businesses and industry leaders.