ML and DL – Difference Between ML and DL Best Classified
Machine Learning (ML) – ML and DL
ML is a subset of AI that uses statistical learning algorithms to build smart systems. The ML systems can automatically learn and improve without explicitly being programmed. The recommendation systems on music and video streaming services are examples of ML. The machine learning algorithms are classified into three categories: supervised, unsupervised and reinforcement learning.
Deep Learning (DL) – ML and DL
This subset of AI is a technique that is inspired by the way a human brain filters information. It is associated with learning from examples. DL systems help a computer model filter the input data through layers to predict and classify information. Deep Learning processes information in the same manner as the human brain. It is used in technologies such as driver-less cars. DL network architectures are classified into Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.
Machine learning and deep learning are on a rage! All of a sudden everyone is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms.
Deep Learning vs. Machine Learning – The Essential Differences You Need To Know!
If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these terms in detail. Then I have gone ahead to compare both of them and explained where we can use them.
Comparison of Machine Learning and Deep Learning (ML and DL)
Now that you have understood an overview of Machine Learning and Deep Learning, we will take a few important points and compare the two techniques.
The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don’t perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly. On the other hand, traditional machine learning algorithms with their handcrafted rules prevail in this scenario. The below image summarizes this fact.
Deep learning algorithms heavily depend on high-end machines, contrary to traditional machine learning algorithms, which can work on low-end machines. This is because the requirements of a deep learning algorithm include GPUs which are an integral part of its working. Deep learning algorithms inherently do a large amount of matrix multiplication operations. These operations can be efficiently optimized using a GPU because GPU is built for this purpose.
Feature engineering is a process of putting domain knowledge into the creation of feature extractors to reduce the complexity of the data and make patterns more visible to learning algorithms to work. This process is difficult and expensive in terms of time and expertise.
In Machine learning, most of the applied features need to be identifing by an expert and then hand-coded as per the domain and data type.
For example, features can be pixel values, shape, textures, position and orientation. The performance of most Machine Learning algorithms depends on how accurately the features are identifiing and extracting.
Deep learning algorithms try to learn high-level features from data. This is a very distinctive part of Deep Learning and a major step ahead of traditional Machine Learning. Therefore, deep learning reduces the task of developing a new feature extractor for every problem. Like, Convolutional NN will try to learn low-level features such as edges and lines in early layers then parts of faces of people and then a high-level representation of a face.
Problem Solving approach
When solving a problem using a traditional machine learning algorithm, it is generally recommending to break the problem down into different parts, solve them individually and combine them to get the result. Deep learning in contrast advocates solving the problem end-to-end. Let’s take an example to understand this. Suppose you have a task of multiple object detection. The task is to identify what is the object and where is it present in the image.
The above article would have given you an overview of Machine Learning and Deep Learning and the difference between them. In this section, I’m sharing my views on how Machine Learning and Deep Learning would progress in the future.
- First of all, seeing the increasing trend of using data science and machine learning in the industry, it will become increasing important for each company who wants to survive to inculcate Machine Learning in their business. Also, each and every individual would be expecting to know the basics terminologies.
- Deep learning is surprising us each and every day, and will continue to do so in the near future. This is because Deep Learning is proving to be one of the best technique to be discovering with state-of-the-art performances.
- Research is continuous in Machine Learning and Deep Learning. But unlike in previous years, where research is limiting to academia, research in Machine Learning and Deep Learning is exploding in both industry and academia. And with more funds available than ever before, it is more likely to be a keynote in human development overall.
I personally follow these trends closely. I generally get a scoop from Machine Learning/Deep Learning newsletters, which keep me updated with recent happenings. Along with this, I follow arxiv papers and their respective code, which are publishing every day.
In this article, we had a high-level overview and comparison between deep learning and machine learning techniques. I hope I could motivate you to learn further in machine learning and deep learning. Here is the learning path for machine learning & deep learning Learning path for machine learning and the Learning path for deep learning.