| What is Deep Learning? |
| What Is a Multi-layer Perceptron(MLP)? |
| What Is the Role of Activation Functions in a Neural Network? |
| What is the meaning of term weight initialization in neural networks? |
| What is the use of the Activation function? |
| What is a binary step function? |
| What is the sigmoid function? |
| What is Tanh function? |
| What is ReLU function? |
| What is the use of leaky ReLU function? |
| What is the softmax function? |
| What is a Swish function? |
| What is the most used activation function? |
| What is Model Capacity? |
| What is the cost function? |
| What is matrix element-wise multiplication? Explain with an example. |
| What is an RNN? |
| What is Vanishing Gradient? |
| What is Exploding Gradient? |
| What is Single-Layer Perceptron? |
| What is Multilayer Perceptrons? |
| What are the main differences between AI, Machine Learning, and Deep Learning? |
| What are the applications of deep learning? |
| What are the deep learning frameworks or tools? |
| What are the disadvantages of deep learning? |
| What are the prerequisites for starting in Deep Learning? |
| What are the supervised learning algorithms in Deep learning? |
| What are the unsupervised learning algorithms in Deep learning? |
| What are the main benefits of Mini-batch Gradient Descent? |
| What are the issues faced while training in Recurrent Networks? |
| What are the different layers of Autoencoders? Explain briefly. |
| What are the three steps to developing the necessary assumption structure in Deep learning? |
| Can Relu function be used in output layer? |
| Differentiate supervised and unsupervised deep learning procedures. |
| Do you think that deep network is better than a shallow one? |
| Explain Data Normalization. |
| Explain gradient descent ? |
| Explain the following variant of Gradient Descent: Stochastic, Batch, and Mini-batch? |
| Explain the different layers of CNN. |
| Explain the importance of LSTM. |
| How many layers in the neural network? |
| How many types of activation function are available? |
| In which layer softmax activation function used? |
| What do you understand by Autoencoder? |
| What do you mean by Dropout? |
| What do you understand by Tensors? |
| What do you understand by Boltzmann Machine? |
| What do you understand by a convolutional neural network? |
| What do you understand by Deep Autoencoders? |
| What do you understand by Perceptron? Also, explain its type. |
| Why is zero initialization not a good weight initialization process? |
| What is mean by deep learning? |
| What is the cost function? |
| What are the benefits of mini-batch gradient descent? |
| What is mean by gradient descent? |
| What is meant by a backpropagation? |
| What is means by convex hull? |
| What is means by auto-encoder? |
| What are the difference Algorithm techniques in Machine Learning? |
| What is the advantage of Naive Bayes? |
| What are the function using Supervised Learning? |
| What are the functions using Unsupervised Learning? |
| What are the roles of activation function? |
| What is Overfitting in Machine Learning? |
| What are the conditions when Overfitting happens? |
| What are the advantages of decision trees? |
| What are parametric models and Non-Parametric models? |
| What are some different cases uses of machine learning algorithms can be used? |
| What is bootstrap sampling? |
| What is permutation sampling? |
| What is total sum of squares? |
| What is sum of squares within? |
| What is sum of squares between? |
| What is p value? |
| What is R^2 value? |
| What does it mean to have a high R^2 value? |
| What are residuals in a regression model? |
| What are fitted values, calculate fitted value for Y=7X+8, when X =5? |
| What pattern should residual vs fitted plots show in a regression analysis? |
| What is overfitting and underfitting? |
| What is type 1 and type 2 errors? |
| What is ensemble learning? |
| What is the difference between supervised and unsupervised machine learning algorithms? |
| What is named entity recognition? |
| What is tf-idf? |
| What is the difference between regression and deep neural networks, is regression better than neural networks? |
| What is the use of activation functions in neural networks? |
| What is gradient descent? |
| What is learning rate in gradient descent? |
| What is a batch in deep neural networks? |
| What is an epoch in Deep neural networks? |
| What is stocastic gradient descent? |
| What is data normalization? |
| What is model capacity? |
| What is regularization? |
| What are hyper parameters in deep neural networks? |
| What is dropout in deep neural networks? |
| What are auto encodes? |
| What is homoscadasticity and heteroscadasticity? |
| What Are Hyperparameters? |
| What are some applications of Recurrent Neural Network? |
| What are the 2 layers of restricted Boltzmann machine called? |
| What Are the Applications of a Recurrent Neural Network (RNN)? |
| What are the five popular algorithms of machine learning? |
| What are the prerequisites for starting out in Deep Learning? |
| What Do You Understand by Backpropagation? |
| What does batch normalization do? |
| What is an algorithm in machine learning? |
| What is autoencoder? |
| What is classification? |
| What Is Dropout and Batch Normalization? |
| What is Fourier transform? |
| What is regression? |
| What Is the Boltzmann Machine? |
| What Is the Difference Between a Feedforward Neural Network and Recurrent Neural Network? |
| What is weight initialization in neural networks? |
| What Will Happen If the Learning Rate Is Set Too Low or Too High? |
| What is a Neural Network? |
| What Is Data Normalization, and Why Do We Need It? |
| What Is Deep Learning? |
| What is gradient descent in machine learning? |
| What is Machine Learning in industry. |
| What is dying neuron problem? |
| What is vanishing gradients? |
| What is exploding gradient descent? |
| What is ‘overfitting’ in the specific field? |
| What is univariate multivariate and bivariate analysis? |
| What is precision and recall? |
| What is the Difference Between AI, Machine Learning, and Deep Learning? |
| Name two activation functions used in deep neural networks? |
| Name the several initiatives used in the particular field |
| Why is batch normalization important? |
| Why are deep networks better than shallow ones? |
| Why do we use gradient descent? |
| Which data visualization libraries do you use and why they are useful? |
| which layer in a deep learning model would capture a more complex or higher order interaction? |
| How will do handle the missing data? |
| How do you understanding Machine Learning Concepts? |
| How can you avoid overfitting? |
| How does missing value imputation lead to selection bias? |
| How are node values calculated in a feed forward neural network? |
| How are the weights calculated which determine interactions in neural networks? |
| How does slope of tangent to the curve of loss function vs weigts help us in getting optimal weights for a neural network |
| How does ReLU activation function works? Define its value for -5 and +7 |
| How can we normalize the data? State a method used for the same? |
| How many layers does Neural networks consist? |
| Where do you regularly source data-sets? |
| Difference between supervised and unsupervised machine learning? |
| Difference between adjusted R^2 and R^2 |
| Difference between collinearity and correlation? |
| In case residual vs fitted plots is showing a pattern and is not distributed evenly or has some outliers how should it be handled? |
| Definition of Boltzmann Machine? |
| Do you have experience including Spark about big data tools for machine learning? |
| If in backward propagation you have gone through 9 iterations of calculating slopes and updated the weights simultaneously, how many times you must have done forward propagation? |
| Imagine a loss function vs weights plot depicting a gradient descent. At what point of the curve would we achieve optimal weights? |
| Imagine you have 2000 training samples and batch size is set to 200 how many iterations will it take to complete 1 epoch? |
| List some commercial practical applications of ANN? |
| List some real-life applications that involve deep learning? |
| State one of the finest procedures often utilized to overcome the issue of overfitting |
01 January 2021
#Deep_Learning
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