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Deep Learning Fundamentals cognitive exam Answers:-

Course Name :- Deep Learning Fundamentals

Module 1 :- Introduction to Deep Learning

Question 1 : Select the reason(s) for using a Deep Neural Network.

  • Some patterns are very complex and can’t be deciphered precisely by alternate means
  • Deep Nets are great at recognizing patterns and using them as building blocks in deciphering inputs
  • We finally have the technology – GPUs – to accelerate the training process by several folds of magnitude
  • All of the above

Question 2: What is TRUE about the functions of a Multi Layer Perceptron?

  • The first neural nets that were born out of the need to address the inaccuracy of an early classifier, the perceptron.
  • It predicts which group a given set of inputs falls into.
  • It generates a score that determines the confidence level of the prediction.
  • All of above

Question 3: Why is the vanishing gradient a problem?

  • Training is quick if the gradient is large and slow if its small
  • With backprop, the gradient becomes smaller as it works back through the net
  • The gradient is calculated multiplying two numbers between 0 and 1
  • All of above.

Module 2 :-deep leaning models

Question 1: For Unsupervised Learning, which of the following deep nets would you choose?

  • Autoencoder or Restricted Boltzmann Machines
  • Deep Belief Nets
  • Convolutional Nets
  • Recurrent Nets

Question 2: True or False: The RELU activation has no effect on back-propagation and the vanishing gradient.

  • True
  • False

Question 3 : True or False: Convolutional Nets are the right model when dealing with data that changes over time because of their built-in feedback loop, allowing them to serve as a forecasting engine.

  • True
  • False

Module 3 :- Additional deep learning models

Question 1 : Which of the following are use cases of Deep nets?

  • Sentiment Analysis of text data.
  • Offering personalized ads based on user activity history.
  • Flagging a transaction as fraudulent.
  • Analyze and segment customers based on digital activity and footprint.
  • Using satellite feeds and sensor data to detect changes in environmental conditions.
  • All of the above.

Question 2: Which of the following are use cases of machine vision. Select all that apply.

  • Image classification and tagging
  • Sentiment Analysis
  • Face Detection
  • Video Recognition
  • Speech Recognition

Question 3 : Which of the following is a good application of an RNTN?

  • If the patterns change through time
  • For general classification problems
  • If there is an unknown hierarchy inherent in the input features
  • For Supervised Fine-tuning
  • To determine the relative importance in the input features

Module 4 : – Deep learning platforms and libraries

Question 1 : Which of the following is not an aspect of a deep net platform?

  • Choice of deep net models
  • Ability to integrate data from multiple sources
  • Manage deep net models from the UI
  • Under the hood performance enhancements to allow for fast training and execution
  • Deriving the optimal hyper-parameter configuration

Question 2: What are the different aspects of a Deep Learning Library?

  • They are a set of pre-built functions and modules that you can call through your own programs
  • Usually maintained by high-performance teams and are regularly updated
  • Most are open source and have a large community that contribute to the code base
  • All of above.

Question 3: True or False: Theano, Caffe, and TensorFlow are examples of deep learning platforms.

  • True
  • False

Deep Learning Fundamentals  final exam answers:-

Question 1: For supervised learning, which of the following deep nets would you choose?

  • Autoencoder
  • Deep Belief Nets
  • Convolutional Nets
  • Restricted Boltzmann Machines
  • Recurrent Nets

Question 2: Which of the following is true with respect to the training process of a deep net?

  • The Cost is the difference between the net’s predicted and actual outputs.
  • The training process utilizes gradients which measure the rate at which the weights and biases change with respect to the cost.
  • The objective of the training process is to make the cost as low as possible.
  • The training process utilizes a technique called back-propagation.
  • All of above.

Question 3: True or False: With backprop, the early layers train slower than the later ones, making the early layers incapable of accurately identifying the pattern building blocks needed to decipher the full pattern.

  • True
  • False

Question 4: For image recognition, which of the following deep nets would you choose? Select all that apply.

  • Autoencoder
  • Deep Belief Nets
  • Convolutional Nets
  • Restricted Boltzmann Machines
  • Recurrent Nets

Question 5 : How does the Deep Belief Network (DBN) solve the vanishing gradient? Select all that apply.

  • It uses a stack of RBMs to determine the initial weights and biases, where the output of any RBM forms the input to the next RBM.
  • It uses a small labelled data set to associate patterns learned by the RBMs to classes.
  • It utilizes supervised fine-tuning, resulting in tweaks in weights and biases and a slight improvement in accuracy.
  • It quickly moves through solution states – set of weights and biases – going from one to another based on a reward.
  • The complete process – RBMs for pre-training and supervised fine-tuning – results in a very accurate net which trains in an acceptable time.

Question 6: True or False: To train, a DBN combines two Learning methods – supervised and unsupervised.

  • True
  • False

Question 7 : Which of the following is the most popular use of a Convolutional Net?

  • Image Recognition
  • Object Recognition in an Image
  • Time Series Forecasting
  • Supervised Fine Tuning
  • General classification

Question 8 : Which of the following are True about a RBM? Select all that apply.

  • The RBM is part of the first attempt at beating the vanishing gradient and uses unlabelled data.
  • It improves its own accuracy through self-correction.
  • Its purpose is to re-create inputs and in doing so has to make decisions about which input features are more important.
  • It stores the relative importance of the features as weights and biases.
  • It predicts which group a given set of inputs falls into.

Question 9 : Which of the following statements are true about the architecture of a CNN? Select all that apply.

  • A CNN can only have two types of layers: CONV and RELU.
  • A RELU layer has to always be followed by a POOL layer.
  • FC layers are usually found at the end.
  • A CONV layer has a theoretical maximum number of filters.
  • A typical CNN implementation has multiple repetitions of CONV, RELU and POOL layers, with sub-repetitions.

Question 10 : True or False: By definition, the classifier in the nodes of an MLP cannot be anything other than the Perceptron.

  • True
  • False

Question 11: Which of the following are differences between a Recurrent Net and a Feedforward Net? Select all that apply.

  • Recurrent Nets feed the output of any time step back in as input for the next step.
  • Recurrent Nets are used for time series forecasting.
  • Recurrent Nets can output a sequence of values.
  • Recurrent Nets are trained using back-propagation.
  • The nodes in a recurrent nets have a classifier that activate and produce a score.

Question 12: Which of the following statements are true about training a Recurrent Net? Select all that apply.

  • Since RNNs use backprop, the vanishing gradient is a problem.
  • The number of time steps used for training has no bearing on the severity of the vanishing gradient problem.
  • The vanishing gradient can potentially lead to decay of information through time.
  • The most popular technique to address the vanishing gradient is the use of gates.
  • The only technique to address the vanishing gradient is the use of gates.

Question 13 : True or False: Deep Autoencoders are used for dimensionality reduction.

  • True
  • False

Question 14 : Which of the following are true about Autoencoders? Select all that apply.

  • It improves its own accuracy through self-correction.
  • Its purpose is to re-create inputs and in doing so has to make decisions about which input features are more important.
  • A Restricted Boltzmann Machine is a type of Autoencoders.
  • It stores the relative importance of the features as weights and biases.
  • It predicts which group a given set of inputs falls into.

Question 15 :True or False: Given they are mainly about machine vision, Convolutional Nets don’t really find a home in the field of medicine.

  • True
  • False

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