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Deep Learning with TensorFlow Cognitive Class Exam Answers:-

Course  Name : Deep Learning with TensorFlow

Module 1:- Intro to Tensorflow

Question 1 : Which statement is FALSE about TensorFlow?

  • TensorFlow is well suited for handling Deep Learning Problems
  • TensorFlow library is not proper for handling Machine Learning Problems
  • TensorFlow has a C/C++ backend as well as Python modules
  • TensorFlow is an open source library
  • All of the above

Question  2 : What is a Data Flow Graph?

  • A representation of data dependencies between operations
  • A cartesian (x,y) chart
  • A graphics user interface
  • A flowchart describing an algorithm
  • None of the above

Question 3 : What is the main reasons of increasing popularity of Deep Learning?

  • The advances in machine learning algorithms and research.
  • The availability of massive amounts of data for training computer systems.
  • The dramatic increases in computer processing capabilities.
  • All of the above

Question  4 : Which statement is TRUE about TensorFlow?

  • Runs on CPU and GPU
  • Runs on CPU only
  • Runs on GPU only

Question  5 : Why is TensorFlow the proper library for Deep Learning?

  • It will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers
  • It provides a collection of trainable mathematical functions that are useful for neural networks.
  • It has extensive built-in support for deep learning
  • All of the above

Module 2 :- Convolutional  Networks

Question  1 : What can be achieved with “convolution” operations on Images?

  • Noise Filtering
  • Image Smoothing
  • Image Blurring
  • Edge Detection
  • All of the above

Question  2 : For convolution, it is better to store images in a TensorFlow Graph as:

  • Placeholder
  • CSV file
  • Numpy array
  • Variable
  • None of the above

Question  3 : Which of the following statements is TRUE about Convolution Neural Networks (CNNs)?

  • CNN can be applied ONLY on Image and Text data
  • CNN can be applied on ANY 2D and 3D array of data
  • CNN can be applied ONLY on Text and Speech data
  • CNN can be applied ONLY on Image data
  • All of the above

Question  4 : Which of the following Layers can be part of Convolution Neural Networks (CNNs)

  • Dropout
  • Softmax         
  • Maxpooling
  • Relu
  • All of the above

Module 3 :- Recurrent Neural Networks

Question 1 : What is a Recurrent Neural Network?

  • A Neural Network that can recur to itself, and is proper for handling sequential data
  • An infinite layered Neural Network which is proper for handling structured data
  • A special kind of Neural Network to predict weather
  • A markovian model to handle temporal data

Question  2 : What is NOT TRUE about RNNs?

  • RNNs are VERY suitable for sequential data.
  • RNNs need to keep track of states, which is computationally expensive.
  • RNNs are very robust against vanishing gradient problem.

Question 3 : What application(s) is(are) suitable for RNNs?

  • Estimating temperatures from weather data
  • Natural Language Processing
  • Video context retriever
  • Speech Recognition
  • All of the above

Question  4 : Why are RNNs susceptible to issues with their gradients?

  • Numerical computation of gradients can drive into instabilities
  • Gradients can quickly drop and stabilize at near zero
  • Propagation of errors due to the recurrent characteristic
  • Gradients can grow exponentially
  • All of the above

Question  5 : What is TRUE about LSTM gates?

  • The Read Gate in LSTM, determine how much old information to forget
  • The Write Gate in LSTM, reads data from the memory cell and sends that data back to the network.
  • The Forget Gate, in LSTM maintains or deletes data from the information cell.
  • The Read Gate in LSTM, is responsible for writing data into the memory cell.

Module 4 :- Restricted Boltzmann Machine

Question  1 : What is the main application of RBM?

  • Data dimensionality reduction
  • Feature extraction
  • Collaborative filtering
  • All of the above

Question  2 : How many layers does an RBM (Restricted Boltzmann Machine) have?

  • Infinte
  • 4
  • 2
  • 3
  • All of the above

Question  3 : How does an RBM compare to a PCA?

  • RBM cannot reduce dimensionality
  • PCA cannot generate original data
  • PCA is another type of Neural Network
  • Both can regenerate input data
  • All of the above

Question  4 : Which statement is TRUE about RBM?

  • It is a Boltzmann machine, but with no connections between nodes in the same layer
  • Each node in the first layer has a bias
  • The RBM reconstructs data by making several forward and backward passes between the visible and hidden layers
  • At the hidden layer’s nodes, X is multiplied by a W (weight matrix) and added to h_bias
  • All of the above

Question  5 : Which statement is TRUE statement about an RBM?

  • The objective function is to maximize the likelihood of our data being drawn from the reconstructed data distribution
  • The Negative phase of an RBM decreases the probability of samples generated by the model
  • Contrastive Divergence (CD) is used to approximate the negative phase of an RBM
  • The Positive phase of an RBM increases the probability of training data
  • All of the above

Module 5 :- Autoecoders

Question 1 : what is the difference between Autoencoders and RBMs?

  • Autoencoders are used for supervised learning, but RBMs are used for unsupervised learning.
  • Autoencoders use a deterministic approach, but RBMs use a stochastic approach.
  • Autoencoders have less layeres than RBMs.
  • All of the above

Question  2 : Which of the following problems cannot be solved by Autoencoders:

  • Dimensionality Reduction
  • Time series prediction
  • Image Reconstruction
  • Emotion Detection
  • All of the above

Question  3 : What is TRUE about Autoencoders:

  • Help to Reduce the Curse of Dimensionality
  • Used to Learn the Most important Features in Data
  • Used for Unsupervised Learning
  • All of the Above

Question  4 : What are Autoencoders:

  • A Neural Network that is designed to replace Non-Linear Regression
  • A Neural Network that is trained to attempt to copy its input to its output
  • A Neural Network that learns all the weights by using labeled data
  • A Neural Network where different layer inputs are controlled by gates
  • All of the Above

Question  5 : What is a Deep Autoencoder:

  • An Autoencoder with Multiple Hidden Layers
  • An Autoencoder with multiple input and output layers
  • An Autoencoder stacked with Multiple Visible Layers
  • An Autoencoder stacked with over 1000 layers
  • None of the Above

Deep Learning with TensorFlow  Cognitive Class Final Exam Answers:-

Question  1 : Why use a Data Flow graph to solve Mathematical expressions?

  • To create a pipeline of operations and its corresponding values to be parsed
  • To represent the expression in a human-readable form
  • To show the expression in a GUI
  • Because it is the only way to solve mathematical expressions in a digital computer
  • None of the above

Question  2 : What is an Activation Function?

  • A function that triggers a neuron and generates the outputs
  • A function that models a phenomenon or process
  • A function to normalize the output
  • All of the above
  • None of the above

Question  3 : Why is TensorFlow considered fast and suitable for Deep Learning?

  • It is suitable to operate over large multi-dimensional tensors
  • It runs on CPU
  • Its core is based on C++
  • It runs on GPU
  • All of the above

Question  4 : Can TensorFlow replace Numpy?

  • None of the above
  • No, whatsoever
  • With only Numpy we can’t solve Deep Learning problems, therefore, TensorFlow is required
  • Yes, completely
  • Partially for some operations on tensors, such as minimization

Question  5 : What is FALSE about Convolution Neural Networks (CNNs)?

  • They fully connect to all neurons in all of the layers
  • They connect only to neurons in the local region (kernel size) of input images
  • They build feature maps hierarchically in every layer
  • They are inspired by human visual systems
  • None of the above

Question  6 : What is the meaning of “Strides” in Maxpooling?

  • The number of pixels the kernel should add
  • The number of pixels the kernel should move
  • The size of the kernel
  • The number of pixels the kernel should remove
  • None of the above

Question 7 : What is TRUE about “Padding” in Convolution?

  • size of the input image is reduced for the “VALID” padding
  • Size of the input image is reduced for the “SAME” padding
  • Size of the input image is increased for the “SAME” padding
  • Size of the input image is increased for the “VALID” padding
  • All of the above

Question  8 : Which of the following best describes the Relu Function?

  • (-1,1)
  • (0,5)
  • (0, Max)
  • (-inf,inf)
  • (0,1)

Question  9 : Which are types of Recurrent Neural Networks? (Select all that apply)

  • LSTM
  • Hopfield Network
  • Recursive Neural Network
  • Deep Belief Network
  • Elman Networks and Jordan Networks

Question  10 : Which is TRUE about RNNs?

  • RNNs can predict the future
  • RNNs are VERY suitable for sequential data
  • RNNs are NOT suitable for sequential data
  • RNNs are ONLY suitable for sequential data
  • All of the above

Question  11 : What is the problem with RNNs and gradients?

  • Numerical computation of gradients can drive into instabilities
  • Gradients can quickly drop and stabilize at near zero
  • Propagation of errors due to the recurrent characteristic
  • Gradients can grow exponentially
  • All of the above

Question  12 : What type of RNN would you use in an NLP project to predict the next word in a phrase? (only one is correct)

  • Bi-directional RNN
  • Neural history compressor
  • Long Short-Term Memory
  • Echo state network
  • None of the above

Question  13 : Which one does NOT happen in the “forward pass” in RBM?

  • Making a deterministic decision about returning values into network.
  • Multiplying inputs by weights, and adding an overall bias, in each hidden unit.
  • Applying an activation function on the results in hidden units.
  • Feeding the nework with the input images converted to binary values.

Question  14 : Which one IS NOT a sample of CNN application?

  • Creating art images using pre-trained models
  • Object Detection in images
  • Coloring black and white images
  • Predicting next word in a sentence

Question  15 : Select all possible uses of Autoencoders and RBMs (select all that apply):

  • Clustering
  • Pattern Recognition
  • Dimensionality Reduction
  • Predict data in time series

Question  16 : Which technique is proper for solving Collaborative Filtering problem?

  • DBN
  • RBM
  • CNN
  • RNN

Question  17 : Which statement is TRUE for training Autoencoders?

  • The Size of Last Layer must be at least 10% of the Input Layer Dimension
  • The size of input and Last Layers must be of the Same Dimensions
  • The Last Layer must be Double the size of Input Layer Dimension
  • The Last Layer must be half the size of Input Layer Dimension
  • None of the Above

Question  18 : To Design a Deep Autoencoder Architecture, what factors are to be considered?

  • The size of the centre-most layer has to be close to number of Important Features to be extracted
  • The centre-most layer should have the smallest size compared to all other layers
  • The Network should have an odd number of layers
  • All the layers must be symmetrical with respect to the centre-most layer
  • All of the Above

Question 19 : With is TRUE about Back-propogation?

  • It can be used to train LSTMs
  • It can be used to train CNNs
  • It can be used to train RBMs
  • It can be used to train Autoencoders
  • All of the Above

Question  20 : How can Autoencoders be improved to handle higly non-linear data?

  • By using Genetic Algorithms
  • By adding more Hidden Layers to the Network
  • By using Higher initial Weight Values
  • By using Lower initial Weight Values
  • All of the Above

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