Deep Learning vs Machine Learning – Understand the key differences
With technological advancement, human intelligence is competing with machines in every aspect. In this regard, the two terms Machine Learning and Deep Learning emerged with the growth of technology. They are changing the way we work in real-time using computing devices. However, both are known as subsets of Artificial Intelligence (AI).
In this article, we will discuss the key points of the difference between Deep Learning vs Machine Learning. Let us know the major differences between Deep Learning vs Machine Learning.
What do you mean by Machine Learning?
Machine Learning is the subgroup or branch of Artificial Intelligence (AI) under computer science that learns from data and algorithms to imitate human intelligence. It enables machines to learn from past data and execute tasks with automation. Moreover, Machine Learning models are useful for training and getting accurate outcomes.
However, ML algorithms typically learn from a large dataset, which trains the system to recognize patterns and relations. Once the system has been trained, it can be used to make predictions or decisions based on new data. Further, ML has many applications, from predicting stock prices and weather patterns to identifying fraud and recognizing images. To learn ML algorithms and their usage in real-time, Machine Learning Training can be helpful.
What do you mean by Deep Learning?
It is the superset or subset of Machine Learning. It also works similarly to Machine Learning but with a different approach. It holds a network of various algorithms together called artificial neural networks. Thus, It uses artificial neural networks that imitate how humans think. It is useful to resolve many complex problems through algorithms.
In contrast to ML, DL algorithms can learn the features directly from the data without requiring manual feature extraction. Using multiple layers in a neural network allows for learning various steps of the data. Each layer learns to change the input data to bring it closer to the desired output. Then it using the same data, it makes several predictions.
Also Read: Derive Meaningful Insights from Unified Data on Snowflake
Deep Learning vs Machine Learning
The following are the differences between Deep Learning vs Machine Learning.
Task Execution
Machine Learning algorithms are faster in training the models but take much time to test them. On the other hand, Deep learning models take a long time to train the models, but they execute tests much faster.
Data Usage
Machine Learning algorithms can work even with a smaller amount of data. But It algorithms can work much more efficiently and accurately with huge amounts of data.
Problem-Solving
The traditional ML model divides complex problems into various parts and resolves each separately. But Deep Learning takes the inputs from the given problem data and produces the final results.
Data Types
Machine Learning models deal with a structured form of data, whereas Deep learning models deal with both structured and unstructured data formats.
Best Suits
Machine Learning is the best fit for resolving simple issues, but Deep Learning is mostly useful for resolving most complex issues.
Hardware Requirements
Machine Learning doesn’t deal with a large amount of data. So it can easily work on low-end devices. Deep Learning deals with massive amounts of data to get results. So it requires high-end machines to perform well.
These are a few of the many differences between Deep Learning and Machine Learning.
Deep Learning and Machine Learning Future trends
These AI subsets have endless future as the coming days are based on AI, including Machine Learning and DL. These technologies will greatly impact the industries like financial services, healthcare, banking, manufacturing, etc. So, there will be a great future for these skills ahead. So, both technologies have a good future, as the predictions given by many tech analysts.
Conclusion
The above discussion shows the variance between Deep Learning vs Machine Learning. There are many differences between the two AI subsets. Both try to imitate human intelligence and brain cells to predict the results. Learning these skills can put the individual a step ahead in the competition. We can resolve most complex problems using Machine Learning and Deep Learning models; we can resolve most complex problems. But while choosing the right technology for your business, certain points need to be considered. So, choose the best technology model and get accurate results.
