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Deep Learning Fundamentals Cognitive Class Exam Quiz Answers

Deep Learning Fundamentals Cognitive Class Certification Answers

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.

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

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

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

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 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 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

Introduction to Deep Learning Fundamentals


Deep learning is a subset of machine learning, which in turn is a branch of artificial intelligence (AI) that focuses on training machines to learn from data and make predictions or decisions without explicit programming. Deep learning algorithms attempt to mimic the way the human brain works by using artificial neural networks composed of interconnected layers of nodes, called neurons.

Here are some fundamental concepts in deep learning:

  1. Neural Networks: At the core of deep learning are artificial neural networks, which are mathematical models inspired by the biological neural networks in the human brain. These networks consist of layers of interconnected nodes (neurons) that process and transform input data.
  2. Deep Neural Networks (DNNs): Deep neural networks are neural networks with multiple layers between the input and output layers. These layers enable the network to learn increasingly complex features and representations of the input data.
  3. Activation Functions: Neurons in a neural network use activation functions to introduce non-linearity into the model, allowing it to learn complex patterns in the data. Common activation functions include the sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax functions.
  4. Training Data: Deep learning models require a large amount of labeled training data to learn from. The model learns to make predictions by adjusting its parameters through a process called backpropagation, which involves minimizing a loss function that measures the difference between the model’s predictions and the true labels in the training data.
  5. Loss Functions: Loss functions quantify how well the model’s predictions match the true labels in the training data. Common loss functions include mean squared error (MSE) for regression tasks and categorical cross-entropy for classification tasks.
  6. Gradient Descent: Gradient descent is an optimization algorithm used to minimize the loss function by adjusting the parameters of the neural network in the direction that decreases the loss. Stochastic gradient descent (SGD) and its variants, such as Adam and RMSprop, are commonly used optimization algorithms in deep learning.
  7. Overfitting and Regularization: Deep learning models are prone to overfitting, where they perform well on the training data but generalize poorly to unseen data. Regularization techniques, such as dropout and L1/L2 regularization, are used to prevent overfitting by penalizing overly complex models.
  8. Validation and Testing: After training a deep learning model on the training data, it is evaluated on a separate validation dataset to tune hyperparameters and prevent overfitting. Finally, the model’s performance is assessed on a testing dataset to estimate its generalization ability.

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