Deep Learning Interview Questions And Answers | AI & Deep Learning Interview Questions | Intellipaat – YouTube

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In this deep learning interview questions and answers you will learn the latest and top questions asked by companies for deep learning interview. This deep learning interview questions & answers video covers all kinds of questions starting from basic to advanced questions so that you can get benefited.
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đź“ťFollowing questions are covered in this deep learning video:
00:00 – Deep Learning Interview Questions And Answers
00:59 – What is the Difference between Machine Learning and Deep Learning?
02:16 – What is Perceptron?
03:26 – How is Deep Learning better than Machine Learning?
04:29 – What are some of the most used applications of Deep Learning?
05:27 – What is the meaning of Over fitting?
06:47 – What are Activation functions?
08:00 – Why is Fourier transform used in Deep Learning?
08:55 – What are the steps involved in training a perceptron in Deep learning?
09:47 – What is the use of the loss function?
10:30 – What are some of the Deep Learning Frameworks or tools that you have used?
11:49 – What is the use of the swish function?
12:38 – What are auto encoders?
13:41 – What are the steps to be followed to use the gradient descent algorithm?
14:57 – Differentiate between a single layer perceptron and a multi-layer perceptron
16:00 – What is data normalization in Deep Learning?
16:54 – What is forward propagation?
17:44 – What is back propagation?
18:40 – What are Hyper parameters in Deep Learning?
19:19 – How can hyper parameters be trained in neural networks?
21:38 – What is the meaning of dropout in Deep Learning?
22:42 – What are Tensors?
23:44 – What is the meaning of model capacity in Deep Learning?
24:33 – What is Boltzmann Machine?
25:25 – What are some of the advantages of using TensorFlow?
26:27 – What is the computational graph in Deep Learning?
27:40 – What is a CNN?
28:25 – What are the various layers present in a CNN?
30:19 – What is an RNN in Deep Learning?
31:15 – What is a Vanishing gradient when using RNNs?
32:11 – What is exploding gradient descent in Deep Learning?
33:10 – What is the use of LSTM?
34:04 – Where are autoencoders used?
35:05 – What are the types of auto encoders?
35:35 – What is a restricted Boltzmann Machine?
36:30 – What are some of the limitations of Deep Learning?
38:04 – What are the variants of gradient descent?
39:33 – Why is mini-batch gradient descent so popular?
40:35 – What are deep autoencoders?
41:47 – Why is the leaky ReLu function used in Deep Learning?
42:35 – What are some of the examples if the supervised learning algorithms in Deep Learning?
43:25 – What are some of the examples of unsupervised learning algorithms in Deep Learning?
43:56 – Can we initialize the weights of a network to start from zero?
45:00 – What is the meaning of valid padding and same padding in CNN?
46:16 – What are some of the applications of transfer learning in Deep Learning?
47:25 – How is the transformer architecture better than RNNs in Deep Learning?
48:41 – What are the steps involved in the working of an LSTM network?
50:07 – What are the elements in TensorFlow that are programmable?
50:43 – What is the meaning of bagging and boosting in Deep Learning?
51:52 – What are generative adversarial networks (GANs)?

53:00 – Have you earned any sort of Certification to improve your learning and implementation process?

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Author: Aprit Dixit

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