Deep Learning with TensorFlow Cognitive Class Exam Quiz Answers

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Deep Learning with TensorFlow Cognitive Class Certificate Answers

Module 1 – Intro to Tensorflow Quiz Answers – Cognitive Class

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 opensource 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 Quiz Answers – Cognitive Class

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

Question 5: The objective of the Activation Function is to:

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

Module 3 – Recurrent Neural Networks Quiz Answers – Cognitive Class

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 Quiz Answers – Cognitive Class

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?

Infinite
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 Quiz Answers – Cognitive Class

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 layers 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 labelled 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 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 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 network 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 highly 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

Introduction to Deep Learning with TensorFlow

Deep Learning with TensorFlow involves using the TensorFlow library, an open-source machine learning framework developed by the Google Brain team. TensorFlow is widely used for building and training deep learning models. Below are the fundamental steps involved in implementing deep learning with TensorFlow:

1. Installation:

Install TensorFlow using the appropriate method for your system, such as pip for Python environments. You can install the CPU version for basic usage or the GPU version for accelerated training on compatible hardware.

2. Import TensorFlow:

Import TensorFlow in your Python script or Jupyter Notebook.

3. Define a Neural Network:

Create a neural network architecture using TensorFlow’s high-level API, Keras. Define the layers, activation functions, and other configurations.

4. Compile the Model:

Compile the model by specifying the optimizer, loss function, and evaluation metric.

5. Data Preparation:

Prepare your dataset for training and testing. This may involve loading data, preprocessing, and splitting into training and validation sets.

6. Training:

Train the model on your training dataset using the fit method.

7. Evaluation:

Evaluate the trained model on the test dataset.

8. Prediction:

Make predictions using the trained model.

9. Save and Load Models:

Save the trained model for future use.

10. TensorFlow 2.x Eager Execution:

TensorFlow 2.x supports eager execution by default, allowing for more intuitive and dynamic model building.

Additional Considerations:

Fine-tuning hyperparameters, adjusting model architecture, and handling more complex scenarios like image classification, natural language processing, and reinforcement learning involve additional considerations specific to each use case.

These steps provide a basic outline for working with TensorFlow for deep learning tasks. Depending on the complexity of your project, you may need to explore advanced features and techniques offered by TensorFlow, such as custom layers, model subclassing, and transfer learning. The TensorFlow documentation and online resources provide detailed guidance on these advanced topics.