Artificial Intelligence Interview Questions and Answers
Question - 51 : - What is TensorFlow?
Answer - 51 : -
TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.
Question - 52 : - Define LSTM.
Answer - 52 : -
Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.
Question - 53 : - List the key components of LSTM.
Answer - 53 : -
- Gates (forget, Memory, update, and Read)
- Tanh(x) (values between −1 and 1)
- Sigmoid(x) (values between 0 and 1)
Question - 54 : - List the variants of RNN.
Answer - 54 : -
- LSTM: Long Short-term Memory
- GRU: Gated Recurrent Unit
- End-to-end Network
- Memory Network
Question - 55 : - What is an autoencoder? Name a few applications.
Answer - 55 : -
An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:
- Data denoising
- Dimensionality reduction
- Image reconstruction
- Image colorization
Question - 56 : - What are the components of the generative adversarial network (GAN)? How do you deploy it?
Answer - 56 : -
Components of GAN:
Deployment Steps:
- Train the model
- Validate and finalize the model
- Save the model
- Load the saved model for the next prediction
Question - 57 : - What are the steps involved in the gradient descent algorithm?
Answer - 57 : -
Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.
Step 1: Allocate weights (x,y) with random values and calculate the error (SSE)
Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized
Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized
Step 4: Use new weights for prediction and calculating the new SSE
Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error
Question - 58 : - What do you understand by session in TensorFlow?
Answer - 58 : -
Syntax: Class Session
It is a class for running TensorFlow operations. The environment is encapsulated in the session object wherein the operation objects are executed and Tensor objects are evaluated.
# Build a graph
x = tf.constant(2.0)
y = tf.constant(5.0)
z = x * y
# Launch the graph in a session
sess = tf.Session()
# Evaluate the tensor `z`
print(sess.run(z))
Question - 59 : - What do you mean by TensorFlow cluster?
Answer - 59 : -
TensorFlow cluster is a set of ‘tasks’ that participate in the distributed execution of a TensorFlow graph. Each task is associated with a TensorFlow server, which contains a ‘master’ that can be used to create sessions and a ‘worker’ that executes operations in the graph. A cluster can also be divided into one or more ‘jobs’, where each job contains one or more tasks.
Question - 60 : - How to run TensorFlow on Hadoop?
Answer - 60 : -
To use HDFS with TensorFlow, we need to change the file path for reading and writing data to an HDFS path. For example:
filename_queue = tf.train.string_input_producer([
"hdfs://namenode:8020/path/to/file1.csv",
"hdfs://namenode:8020/path/to/file2.csv",
])