Keras Interview Questions and Answers
Question - 41 : - What Are the Applications of a Recurrent Neural Network (RNN)?
Answer - 41 : - The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural Networks can also address time series problems such as predicting the prices of stocks in a month or quarter.
Question - 42 : - What Is the Difference Between Batch Gradient Descent and Stochastic Gradient Descent?
Answer - 42 : - 
Question - 43 : - What is Overfitting and Underfitting, and How to Combat Them?
Answer - 43 : -
Overfitting occurs when the model learns the details and noise in the training data to the degree that it adversely impacts the execution of the model on new information. It is more likely to occur with nonlinear models that have more flexibility when learning a target function. An example would be if a model is looking at cars and trucks, but only recognizes trucks that have a specific box shape. It might not be able to notice a flatbed truck because there’s only a particular kind of truck it saw in training. The model performs well on training data, but not in the real world.
Underfitting alludes to a model that is neither well-trained on data nor can generalize to new information. This usually happens when there is less and incorrect data to train a model. Underfitting has both poor performance and accuracy.
To combat overfitting and underfitting, you can resample the data to estimate the model accuracy (k-fold cross-validation) and by having a validation dataset to evaluate the model.
Question - 44 : - How Are Weights Initialized in a Network?
Answer - 44 : -
There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.
Question - 45 : - What Is the Difference Between Epoch, Batch, and Iteration in Deep Learning?
Answer - 45 : -
- Epoch – Represents one iteration over the entire dataset (everything put into the training model).
- Batch – Refers to when we cannot pass the entire dataset into the neural network at once, so we divide the dataset into several batches.
- Iteration – if we have 10,000 images as data and a batch size of 200. then an epoch should run 50 iterations (10,000 divided by 50).
Question - 46 : - Why is Tensorflow the Most Preferred Library in Deep Learning?
Answer - 46 : - Tensorflow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and Torch. Tensorflow supports both CPU and GPU computing devices.
Question - 47 : - Is keras a library?
Answer - 47 : -
Yes, Keras is an open-source neural-network library written in Python.
Question - 48 : - Who invented keras?
Answer - 48 : -
François Chollet invented Keras and he is currently working as an AI Researcher at Google.
Question - 49 : - What are advanced activation functions in keras ?
Answer - 49 : -
LeakyReLU, PReLU
Question - 50 : - What are regularizers in keras ?
Answer - 50 : -
- keras.regularizers.l1(0.)
- keras.regularizers.l2(0.)
- keras.regularizers.l1_l2(l1=0.01, l2=0.01)