Deep Learning and Machine Learning are the current buzzwords that you hear every now and then. All of a sudden everyone is talking about them, even though most of the people don’t even have clear understanding about them. But irrespective of the domain on which you work on, I am sure that you would have heard these terms.
Here is a clear demographics of Google trend for these keywords in which you will get to know the kind of attention these words are getting. You can see how both the keywords are continuously getting more hits.
I can assure that if you stick to this post, so by the end you will get the clear differences between these two interchangeably used words.
Before getting started, I believe you would be familiar with a basic understanding of the two terms Machine Learning and Deep Learning. If you ain’t, then here is a simple definition for you.
If you are aware of the definition feel free to skip to next section.
The main intent of ML is to give machines the power to learn by themselves using the data provided to them. By using these data they will learn the art of prediction.
Machine Learning: Ideally ML is subset of Artificial Intelligence. It is simply a method of training algorithms so that they can learn how to make decisions.
Deep Learning: Now deep learning is a subset of Machine Learning, it’s simply a technique for realizing machine learning. In other words, Deep Learning is an extension of Machine Learning.
Just like our brains identify patterns and classify various types of information. On a similar note, Deep Learning algorithms can be taught to accomplish the same tasks of identifying patterns for machines.
Whenever we receive a new information, the brain tries to make decision by comparing it with previous knowledge and experience — which is the same concept deep learning algorithms employ.
For example, artificial neural networks (ANNs) is a type of algorithms that simply imitates how our brains works.
3 types of Machine Learning algorithms:
- Supervised Learning: In this type of learning the algorithm needs the labelled data. From this labelled data it compares its prediction with actual output and tries to minimize the error using optimization algorithms.
- Unsupervised Learning: These algorithms doesn’t need any labelled data for its processing.
- Reinforcement Learning: These algorithms learns from action reaction analysis. They interact with environment and learn from its reaction.
Deep Neural Network will have 3 types of layers:
- The Input Layer
- The Hidden Layer
- The Output Layer
Neural networks are used to predict the output. The standard understanding is that the neural network learns the pattern of data. After learning patterns it performs predictions that fall in the same line as the pre-specified pattern.
Normally, Deep Learning algorithms depends on high-end machines while traditional machine learning depends on low-end machines.
Thus, Deep Learning requirement includes GPUs. That is an integral part of it’s working. High end GPUs are required because they perform many matrix multiplication while learning.
So we have studied Deep Learning and Machine Learning and also gone through the comparison between the two. We have also give you the comparison sheet with different factors. If you have any questions, feel free to ask in the comments section.
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You can also check out our post on: Unsupervised learning with Python