How to switch from Machine Learning to Deep Learning in 5 steps ?

Machine Learning to Deep Learning

One of the best questions of majority data science aspirants or intermediate data science practicer . Really Deep Learning seems more harder than traditional machine learning. But the truth is if you the basics of deep learning , this transition will be easy for you . My inbox is full of such questions hence today we ( Data Science Learner Team ) decided to create an overview article on – How to switch from Machine Learning to Deep Learning in 5 steps ?

Machine Learning to Deep Learning in 5 steps-

Step 1 – ( Why deep learning ?)

Understand why you need deep leaning over machine learning . See it is not always to require to use deep learning over machine learning . There are only some specific cases when it(Deep Learning ) is best to have or must to have technique .See ! there are some factors which effect this decision making – Size of data , Hardware , Feature Engineering and Execution time etc .If you have some similar concern as we mention above , It is good to go with deep learning otherwise machine learning is best for you .We have a separate article on it . Please go through the article – Difference Between Deep Learning and Machine Learning .

Step 2 – ( Basic understanding for Deep Learning Concept)-

Deep learning is completely on neural networks . Hence you should understand the basic concept of these neural network like gradient descent , loss function , activation function , back propagation , hidden layers etc . Once you understand them it will be easier for you to understand the implementation of ANN , CNN, RNN etc for you .

Step 3 – ( Implement using some high level deep learning framework – keras etc )-

I am telling you that keras has very easy implementation for ann , cnn and rnns .Basically it will give you same experience as Scikit learn gives in machine Learning .   You may create your very complex ann network in less that 25 lines of code . I know it is difficult to believe right ? See here only Model creation is not everything but you need to care of its deployment and performance  stuffs .

Few things are important to answer –

  1. How will you measure model’s performance ?
  2. How will you tune and improve its  this deep learning model ?
  3. How to decide the numbers of hidden layers in deep learning networks ?
  4. Which loss function is perfect for model evaluation etc ?

Step -4 ( How to make training on going in Production ? ) –

Most of the project remains in POC only . They never reach to production level . So most of us never explore this phase in starting few years in Data Science . This is really a good point to thought . Specially from the testing point  of views if it is a integrated part of a product . Where its outcome is next pipeline and results into some actions .

Step -5 ( Use low level implementation ) –

There are so may low level framework for deep learning like tensor flow . These frameworks provide a good control while model development . Obviously it is difficult to implement these but once you understand the concept and basics , It is easy to go with it also . Only some line of code increases .



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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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