I know most of you are confused in two trendy terms Machine Learning and Deep Learning . If you want to really understand the Difference between Deep Learning and Machine Learning , Go for investing your five minutes in this article . I promise this next five minutes will be amazing for you if you are Technology Lover .
Before I Start anything in very straight forward manner , I will say Deep Learning is more powerful and flexible than Traditional Machine Learning . In fact Deep learning is also a Machine Learning type but have difference in many other ways . Do not think that I end the suspense here , There is a complete movie remaining for you . I mean , Traditional Machine learning has its own advantages . You have to choose and decide the best for your application.
What is Machine Learning –
When your code is written in such a way that it learns from previous result and experience and it also improves its performance , It is Machine Learning . If you need to drill more on it read the article about Machine Learning fundamentals . I will discuss more on deep learning in this Article .
What is Deep Learning –
A kind of Machine Learning , When machine learning datasets are large and target function is complex we use Deep Learning . It is basically based on Artificial Neural Network which are multi layered . I know you might not familiar Artificial neural network also . Do not worry lets go more deep into Dee Learning .
If you want to develop a model which can predict numbers of building in any Image . you can use Deep learning here . Suppose you have the data about coordinate for shape and size of buildings . Now just prepare the data set where the features are coordinate of the building .Predicted class as Building or Non Building . Now in deep learning , it automatically choose the weight of features by having multiple Iteration in training the model . Actually it works on the principle of Human Brain design & working Mechanism .Our brain contains multiple neurons . Here in Deep learning , there are artificial neurons in the place of natural neurons .lets have look below in Image , It will make you understand in better way –
Here in the above Image you can see easily , There can be multiple layer between Input and Output as hidden layer . These hidden layer are consist of neurons .Each neurons contains some weight and pass some message to the next layer . These weights changes in every iteration while training the model until the model reaches at a certain accuracy level .
I think , Now you have basic understanding of Deep learning .Lets go for the second step .Now I am going to explore Difference between Deep Learning and Machine Learning.
Difference between Deep Learning and Machine Learning
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Data dependencies for deep learning –
Deep Learning needs more training data than traditional machine learning . So be careful when your application or model have less training data and performing deep learning on this .In the opposite Traditional machine learning demands less data in comparison of deep learning .
2 . Hardware requirement for Deep learning –
Deep Leaning model have the Multiple GPU supported architecture . Actually Deep learning needs complex matrix calculation , That is the major reason why Data scientist prefer high configuration machine in deep learning . In the opposite side , Traditional machine learning kernel can run on lower configuration than deep learning .
3. Time Complexity –
Deep learning model takes more time than Traditional machine learning .Reason is very obvious .I don’t think after reading above two factor you need any more explanation . Difference between Deep Learning and Machine Learning on Time complexity matters a lot on organization level .
4. Feature selection –
Deep learning automatically select the features and assign the weight. While Traditional machine learning involves manual feature selection process . This is one the most impacting Difference between Deep Learning and Machine Learning .
Apart from these four Difference between Deep Learning and Machine Learning , There is one more . It is how deep learning model look into the problem . Most of the deep learning models solves the problem end to end . It is not like breaking the problem into different parts . Traditional machine learning model usually break the problem into several steps . They first solve them one by one and then merge them .
This approach of Traditional machine learning makes the the model interpret-able . Basically In industry , Deep learning model are tested properly for a longer time before launching live . Actually Deep learning model is not interpretable so no body can analyse its prediction behavior after training .
Framework for Deep learning –
Deep learning is trending fast these days because of Best documented and high performance Frameworks in Deep learning . Here is description for top framework of deep learning –
2.Theano
3. caffe
4. Keras
Advantages of Traditional Machine Learning :
In case , You have small data set for training , You are not sure of the performance of your Machine learning model . You want to explore the flow of prediction in case of bad performance after the training . You have low end machines . I think , Going towards Traditional Machine Learning is the best solution .
Trend in Deep Learning –
Google Brain is itself an application for Deep learning . As I have heard some where ( Sorry not remember where )-
” Machine Leaning is eating traditional software and Deep Learning is eating Machine Learning ”
As you can see in the above Image , Interest over the time in Deep Learning is in uptrend .
End Notes-
For designing intelligent system , Deep Learning is becoming an essential tool these days . Where Traditional machine learning has its own specification . Both are powerful and Amazing . I hope this article must be helpful in understanding the Difference between Deep Learning and Machine Learning .
If you like this article please share your thoughts with us . You may also write about your learning experience with Data Science Learner.In this blog series we are planning to launch Learning Series for Deep Learning ( In TensorFlow) . If you excited about this learning series please let us know .
Data Science Learner Team
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