Machine Learning Tutorials, Courses and Certifications
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
