If you are a data software engineer or what the data and software skills then you would get ample opportunities in the field of Deep Learning. Deep Learning is a technology that has become the fastest-growing information technology and companies all over this world use this technology. It is a set of techniques that predict outputs from a layer set of inputs.
There is no doubt that every data or computer engineer would want a Deep Learning job as it comes with many perks and it is a prestigious job as well. But the thing is it’s not a job that you would just get. The companies look for a candidate that knows what they are doing and have a good knowledge of science and data skills. So before applying for a data scientist job in the field of deep learning, you may want to sharpen your skills and knowledge on the subject.
You have to keep in mind that your fate for getting a job would depend on the interview so you must work forward to sharpen your interview skills as well. If you are fresher or even if you a professional, you might get nervous about the interview. But there are a few things that can help you. You should know that knowing what kind of questions would be asked in Deep Learning interview can be a great help to a job candidate.
All you need to look for are the questions that would be asked in Deep Learning interview so that you know what’s coming for you and how much you need to practice more. Since you are here, you must be looking for the Deep Learning interview questions as well.
We are not going to disappoint you and you would get everything you have been looking for in this article. So go ahead and take a look at the following list of best Deep Learning Interview questions so that you can easily prepare for the upcoming interview.
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Deep Learning Interview Questions
Q1.Is It Ok To Connect From A Layer 4 Output Back To A Layer 2 Input?Ans-Yes, this can be done considering that layer 4 output is from previous time step like in RNN. Also, we need to assume that previous input batch is sometimes- correlated with current batch.
Q2.What Is An Auto-encoder?Ans-An autoencoder is an autonomous Machine learning algorithm that uses backpropagation principle, where the target values are set to be equal to the inputs provided. Internally, it has a hidden layer that describes a code used to represent the input.–Some Key Facts about the autoencoder are as follows:-It is an unsupervised ML algorithm similar to Principal Component AnalysisIt minimizes the same objective function as Principal Component AnalysisIt is a neural networkThe neural network’s target output is its input.
Q3.What Is A Model Capacity?Ans-Ability to approximate any given function. The higher model capacity is the larger amount of information that can be stored in the network.
Q4.What Is An Autoencoder?Ans-Autoencoder is artificial neural networks able to learn representation for a set of data (encoding), without any supervision. The network learns by copying its input to the output, typically internal representation has smaller dimensions than input vector so that they can learn efficient ways of representing data. Autoencoder consist of two parts, an encoder tries to fit the inputs to an internal representation and decoder converts internal state to the outputs.
Q5.What Is Data Normalization And Why Do We Need It?Ans-Data normalization is very important preprocessing step, used to rescale values to fit in a specific range to assure better convergence during backpropagation. In general, it boils down to subtracting the mean of each data point and dividing by its standard deviation.
Q6.Weight Initialization In Neural Networks?Ans-Weight initialization is a very important step. Bad weight initialization can prevent a network from learning. Good initialization can lead to quicker convergence and better overall error. Biases can be generally initialized to zero. The general rule for setting the weights is to be close to zero without being too small.
Q7.Why Is Zero Initialization Not A Recommended Weight Initialization Technique?Ans-As a result of setting weights in the network to zero, all the neurons at each layer are producing the same output and the same gradients during backpropagation.The network can’t learn at all because there is no source of asymmetry between neurons. That is why we need to add randomness to weight initialization process.
Q8.What Is The Role Of The Activation Function?Ans-The goal of an activation function is to introduce nonlinearity into the neural network so that it can learn more complex function. Without it, the neural network would be only able to learn function which is a linear combination of its input data.
Q9.Why Are Deep Networks Better Than Shallow Ones?Ans-Both shallow and deep networks are capable of approximating any function. For the same level of accuracy, deeper networks can be much more efficient in terms of computation and number of parameters. Deeper networks are able to create deep representations, at every layer, the network learns a new, more abstract representation of the input.
Q10.What Is A Dropout?Ans-Dropout is a regularization technique for reducing overfitting in neural networks. At each training step we randomly drop out (set to zero) set of nodes, thus we create a different model for each training case, all of these models share weights. It’s a form of model averaging.
Q11.What Is Weight Initialization In Neural Networks?Ans-Weight initialization is one of the very important steps. A bad weight initialization can prevent a network from learning but good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be generally initialized to zero. The rule for setting the weights is to be close to zero without being too small.
So these are some of the most frequently asked Deep Learning Questions that you were looking forward to. We hope that you got everything you have been looking for and now that you have the list of questions that may be asked in the interviews you would easily prepare for the interview as well. Thank you for coming and best of luck with your upcoming interview.