Difference between Machine Learning & Deep Learning

If you are a data scientist or technophile, curious to know about the latest trends in artificial intelligence, then you are at the right place. This article delves into a comprehensive exploration of artificial intelligence (AI) with a short comparative study of the main differences between (ml) machine learning and (dl) deep learning. 

What is Machine Learning?

These days technology has become an integral part of our daily lives, and the use of artificial intelligence (AI) has revolutionized industries worldwide. To keep up the pace, companies around the globe are relying heavily on machine learning algorithms to make their processes fast and easier. Artificial intelligence (AI) is the main system and machine learning (ml) is its subset. Whereas dl (deep learning) is the subfield of ml. The neural networks are computational nodes or neurons that are layered deep into dl algorithms. Each layer has one input layer, one hidden layer, and one output layer.

All dl (deep learning) is ml (machine learning) and all machine learning(ml) is artificial intelligence (ai), but not all ai is ml. While machine learning (ml) and deep learning (dl) terms can be occasionally deemed interchangeable there are stern differences between the two. These terms are not swappable as they have major distinctions. These two technologies ml and dl majorly differ in terms of their data requirements, human intervention, and processing techniques.

In simple terms, machine learning(ml) is the way out to make computers more efficient and intelligent without making them explicitly programmed. Here computers learn from data that is fed to them. In ml models the machines are at the intersection of computer science and statistics that enables self-learning of observed data and application of that data without the need for human help.

What is deep Learning?

Deep learning (dl) is simply an algorithm inspired by the human brain to achieve complex data analysis. It analyses data by using deep artificial neural networks with learned representations simply in the manner a human would look at a problem. Lately, this field is getting significant attention for a good cause.

In traditional machine learning, an algorithm is given a relevant set of patterns of data to analyze. In deep learning, the algorithm is fed with raw data and decides on its own what features are relevant as it tries to mimic the human brain function. Here the more you train the more robust the deep learning system will become. This simple definition is for those who are not well-versed in technical jargon.

Top Points of Difference Between Machine Learning and Deep Learning

1. Functioning & Accuracy

DL algorithm uses multilayered artificial neural networks (ANN) to analyze the data and has conceded successful and challenging projects. This ANN is the biological neural network of the human brain more complex. There are chances of developer or human errors that lead to wrong decisions and low accuracy in machine learning. Whereas machine learning algorithms have a simple structure or a decision tree that focuses on learning capability and making decisions based on data. No human dependence or self-learning capabilities make deep learning follow an end-to-end approach with accuracy.

2. Data Dependency & Performance

Deep learning models are data-hungry by nature; they need data in abundance to train effectively due to their complex multi-layer neural network structure that needs much more datasets. This use of node networks and supercomputers has made deep learning models suitable to solve complex problems with amazing execution. Machine learning on the other hand is designed to perform at a moderate level and give reasonable predictions even when the available data is limited. The performance of ml models is medium as they are suitable to solve simple and a bit complex problems.

3. Training or Execution time

Machine learning algorithms take much less time to train the model, but it takes a much longer duration to test the model. In comparison to ml, deep learning needs intensive computation to train multiple-layered neural networks. Hence taking longer execution time to train the model but it takes less time to test the model. 

4. Feature Extraction & Human Intervention

When a new learning engages in ml models, they need a step of feature extraction by a human expert to make the learning happen. The role of the human expert is to guide or train the machine on what type of feature it must look for. Instead, the deep learning model does not need a feature extractor to solve the problem. Without the need for human intervention, its smart algorithm uses multi-layered neural networks to train the machine. The knowledge transfer from one layer to the next layer lets it learn from the data on its own.

4. Hardware Requirements & Result Interpretation

A low amount of data makes machine learning models work on low-end machines contrary to deep learning models that need high-end machines and GPU’s. The Graphics Processing Unit is small but works like a massively parallel computer that works dedicatedly to carry out multiple tasks simultaneously. Result interpretation for a given problem in a machine learning model is quite easy to understand. It is amazingly easy to know how its result occurred and how the process was.

On the contrary deep learning algorithms are exceedingly difficult to understand. The result interpretation of a particular problem is anyways much better compared to ml but it’s tough to know the reason behind that output.

Conclusion

To conclude, we can say that deep learning plays a pivotal role in various fields, and it can grow exponentially depending on the availability of data and powerful computing. Choosing any one of these algorithms depends on the amount of data and complexity of the problem.