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q2.py
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q2.py
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def is_palindrome(word):
return word.lower()==word[::-1].lower()
print(is_palindrome("hahah"))
Deep Learnig is one of the most interesting domain. It deals with a set of algorithms and models that are used to train machines to develop the capability to predict something ( something which it has never seen before ). Deep learning involves training of a set of parameters which predict the output . Mathematics is an indispensible toolbox of Deep Learning.
Human brains are made up of neurons which interact to give humans the ability to think and learn. Deep Learning is also based on the same concept. It uses Artificial Neurals. The collection of these Neurons is called Artificial Neural Network. Deep Learning can be really creative at times. My favourite , Neural Style transfer , is an application of Convolutional Neural Networks( which is a type of Neural Network ). Another interesting applications of Deep Learning are self driving cars, google assistant, alexa.
Tensorflow and pytorch are the two most popular framworks that are used for Deep Learning. Some higher level APIs like Keras make deep learning really easy . But in my opinion these higher level APIs should be introduced to new learners after they learn to user the lower level APIs like tensorflow. Keras hides the implementation details from the user. So if someone wants to make something new in the professional world, he/she has to write the code in lower level APIs .
Another interesting application is Generative Adversarial Networks (GANs). Imagine if there could be someone who could speak like you, write an essay like you !! All this is possible with Deep Learning.
This essay introduces only some aspects of Deep Learning. In reality , it is far more huge and has applications beyond our imagination !!