Deep Learning with TensorFlow Cognitive Class Exam Quiz Answers

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Get Certificate: Deep Learning with TensorFlow Cognitive Class Exam Quiz Answers

Deep Learning with TensorFlow Cognitive Class Certification Answers

Module 1 – Introduction to TensorFlow

Question 1: Which statement about TensorFlow is FALSE?

• TensorFlow is well suitable for Deep Learning Problems
• TensorFlow is not proper for Machine Learning Problems
• TensorFlow has a C/C++ backend as well as Python modules incorrect
• 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: Which function is NOT used as an Activation Function?

• sigmoid()
• softplus()
• sin()
• tanh()
• relu()

Question 4: Which statement about TensorFlow is TRUE?

• runs on FPGA
• runs on CPU only
• runs on CPU and GPU
• runs on GPU only

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

• It will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers.
• Provides helpful tools to assemble subgraphs common in neural networks and deep learning
• TensorFlow has extensive built-in support for deep learning
• All of the above

Module 2 – Convolutional Networks

Question 1: What can be achieved with “Convolution” operation 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 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 (CNN)?

• 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 (CNN)

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

Question 5: Objective of Activation Function is to:

• Increase the Size of Network
• Handle Non-Linearity in the Network
• Handle Linearity in the Network
• Reduce the Size of Network
• None of the above

Module 3 – Recurrent Neural Network

Question 1: What is a Recurrent Neural Network?

• A Neural Network that can recurs to itself
• An infinite layered Neural Network
• A special kind of Neural Network to predict weather
• A markovian model

Question 2: What is TRUE about RNNs?

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

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

Question 4: Why RNNs are 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 LSTM Stands for?

• Last State Threshold Model
• Limit Sinusoidal Term Memory
• Long Short Term Memory
• Least Squares Topological Minimization
• None of the above

Module 4 – Restricted Boltzmann Machines (RBM)

Question 1: What things we can do with unsupervised learning?

• Data dimensionality reduction
• Object recognition
• Feature extraction
• Pattern recognition
• All of the above

Question 2: How many layers has a RBM (Restricted Boltzmann Machine)?

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

Question 3: How RBM compares to 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: Select the True statement about Restricted means in 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: Select the TRUE statement about RBM:

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

Module 5 – Autoencoders

Question 1: Autoencoders are also known as:

• LSTM Networks
• Diabolo Networks
• Deep Belief Network
• Siamese networks
• None 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 Curse of DImensionality
• Are Shallow Neural Networks
• Used to Learn 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 controled by gates
• All of the Above

Question 5: What is a Deep Autoencoder:

• Autoencoder with Multiple Hidden Layers
• Autoencoder with multiple input and output layers
• Autoencoder stacked with Deep Belief Network
• Autoencoder stacked with
• None of the Above

Deep Learning with TensorFlow Final Exam Answers – Cognitive Class

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 only way to solve mathematical expressions in a digital computer
• None of the above

Question 2: What is an Activation Function

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

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

• it is suitable to operate over large and multidimensional tensors
• runs on CPU
• its core is based on C++
• runs on GPU
• All of the above

Question 4: TensorFlow can replace Numpy?

• None of the above
• No, whatsoever incorrect
• Only with 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(CNN)

• Fully connects to all neurons in all the layers
• connects only to neurons in local region(kernel size) of input image
• builds feature maps hierarchically in every layer
• Inspired by human visual system incorrect
• None of the above

Question 6: What does “Strides” in Maxpooling Mean

• The number of pixels, kernel should add.
• The number of pixels, kernel should be moved.
• The size of kernel.
• The number of pixels, kernel should remove.
• None of the above

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

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

Question 8: Which of the following best describes Relu Function

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

Question 9: Which ones are types of Recurrent Neural Networks?

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

Question 10: What 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 a NLP project to predict the next work 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: How RBM can reduce the number of features?

• By transforming the features using a kernel function
• By randomly filtering out a few features then checking if the input can be regenerated
• By minimizing the difference between inputs and outputs, while weighting the features in the
• By cutting of features with less variance
• All of the above

Question 14: How Autoencoders compares to K-means?

• Autoencoders are always faster than k-means
• Both are based on Neural Networks
• K-Means is always better than Autoencoders
• Both can cluster the data
• None of the Above

Question 15: Select all possible uses of Autoencoders and RBM (select all that applies)

• Predict data in time series
• Pattern Recognition
• Dimensionality Reduction
• Clustering
• All of the above

Question 16: What is TRUE about Collaborative Filtering

• it is a technique used by Recommender Systems
• None of the Above
• It makes automatic predictions for a user by collecting information from many users
• RBM can be used to implement a collaborative filter
• It is Deep Neural Network

Question 17: Which of the statements is TRUE for training Autoencoders:

• The Size of Last Layer must atleast be 10% of Input layer DImension
• The size of input and Last Layers must be of 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 centre most layer has to be close to number of Important Features to be extracted.
• The Centre most Layer should have smallest size compared to all other layers
• The Network should have odd number of Layers
• All the layers must be symmetrical with respect to centre most layer
• All of the Above

Question 19: With is True about Backpropogation:

• Can be used to train LSTM
• Can be used to train CNN
• Can be used to train RBM
• Can be used to train Autoencoders
• All of the Above

Question 20: How Autoencoder can be Improved to handle Higly nonlinear Data:

• Use Genetic Algorithms
• Add more Hidden Layers to the Network
• Use Higher initial Weight Values
• Use lower initial weight Values
• All of the Above

Introduction to Deep Learning with TensorFlow

Deep Learning with TensorFlow is a vast field that encompasses using Google’s TensorFlow library to build and train neural networks for various tasks such as image recognition, natural language processing, and more. TensorFlow is one of the most popular deep learning frameworks due to its flexibility, scalability, and extensive community support.

To get started with Deep Learning using TensorFlow, here’s a high-level roadmap:

1. Installation: Install TensorFlow on your system using pip or Anaconda. Make sure you have the correct version compatible with your hardware (CPU, GPU) and operating system.
2. Basics of TensorFlow: Familiarize yourself with the basics of TensorFlow, such as tensors, operations, graphs, sessions, and variables. TensorFlow operates by building a computational graph that defines the operations and the flow of data.
3. Building Models: Start building simple models using TensorFlow’s high-level APIs like Keras. Keras provides a user-friendly interface for building neural networks, allowing you to quickly prototype models.
4. Data Preparation: Prepare your data for training. This involves data preprocessing steps like normalization, splitting data into training and validation sets, and possibly augmenting the data to increase the size of your training dataset.
5. Model Training: Train your model using TensorFlow. Define the architecture of your neural network, choose an appropriate loss function and optimizer, and train the model on your training data.
6. Evaluation: Evaluate the performance of your model on the validation set to assess its accuracy, precision, recall, etc. Use metrics relevant to your specific task.
7. Fine-tuning and Optimization: Experiment with different hyperparameters, model architectures, and optimization techniques to improve the performance of your model. Techniques like dropout, batch normalization, and learning rate scheduling can help in fine-tuning.
8. Deployment: Once you’re satisfied with your model’s performance, deploy it for inference on new data. This could involve exporting the trained model, setting up an inference pipeline, and integrating it into your application or system.

Throughout this process, it’s important to keep learning and experimenting. TensorFlow’s documentation, tutorials, and community forums are valuable resources for expanding your knowledge and tackling more complex tasks in deep learning.