Deep learning is one of those fields in technology that is capturing the attention of all towards it by its revolutionary results. Here in this article, we will be covering the following points to get started with DL :
- Limitations of Machine learning or causes of the evolution of Deep learning
- What is Deep Learning
- Difference between Artificial Intelligence, Machine learning, and Deep learning
Limitations of Machine Learning
First limitation of Machine learning is high dimensionality of data, now the data that is generated is huge in size so we have a very large number of inputs and outputs and due to that machine learning algorithms fail to perform better so machine learning algorithms cannot deal with high dimensionality of data or data with high number of inputs and outputs.
Another one is it Cannot solve crucial AI problems like NLP, image classification and many more.
One of the biggest challenges with the traditional machine learning model is a process called feature extraction. For complex problems such as object detection, this is a huge problem.
Now suppose we are predicting whether there will be match today or not and for that we have given certain parameters to our machine learning model like whether there will be rain or not , temp and many more but we unfortunately forget to give one parameter that is humidity and our machine learning models are not that efficient that they will generate that feature by there own so this is one of the biggest limitations of machine learning. So to solve that problem new terminology is evolved and that is deep learning.
DL models focus on the right features by themselves, requiring little or no guidance from the programmers. So, with the little guidance what this deep learning model can do is that they can generate their features on which the output depends. These models also solved the dimensionality problem to good extent.
What is Deep Learning
DL is a part or subfield of machine learning that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing model, lower layers or layer which comes first may identify edges, while the layers at a higher level may identify the concepts relevant to a human such as digits or letters or faces.
Difference between Artificial Intelligence, Machine learning, and Deep learning
- Artificial Intelligence:
AI is incorporating human intelligence into machines. Whenever a machine completes tasks based on a set of rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.
- Machine Learning:
Machine Learning is to enable machines to learn by themselves using the provided data and make accurate predictions. It is a method of training algorithms such that they can learn how to make decisions.
- Deep Learning:
In other words, DL is the next evolution of machine learning. Deep Learning algorithms are roughly inspired by the information processing patterns found in the human brain. Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines they are not limited to that only but they can do anything that humans can think.