What You Will Learn
- 1 Deep Learning Interview Questions
- 1.1 What is the difference between perception and logistic Regression?
- 1.2 Can we have the same bias for all the neurons of the invisible layer?
- 1.3 What is supervised and unsupervised tasks in Deep Learning?
- 1.4 What are the most used applications of Deep Learning?
- 1.5 What are activation functions?
- 1.6 What are the steps involved in perceptual training in deep learning?
- 1.7 What is the Boltzmann Machine?
- 1.8 What is the Cost Function?
- 1.9 What is a CNN?
- 1.10 What is the use of LSTM?
- 1.11 What are the elements in TensorFlow?
- 1.12 What is the Bagging and Boosting in Deep Learning?
- 1.13 Why should we use Batch Normalization?
- 1.14 Why do RNNs work better with test data?
- 1.15 How does LSTM solve the vanishing gradient challenge?
- 1.16 What are autoencoders?
- 1.17 What is forward propagation?
- 1.18 What are Tensors?
- 1.19 Why the most preferred library in Deep learning is TensorFlow?
- 1.20 What are Hyperparameters?
Deep Learning Interview Questions
Here are the basic 20 Deep Learning Interview questions that can help you during the interview. Read the full article. Hope it will help you.
What is the difference between perception and logistic Regression?
This the only difference in the limit function, when restricted logistic regression model to give either exactly 1 or exactly 0, we get the preceptor model.
Can we have the same bias for all the neurons of the invisible layer?
Basically, you can put a different value of bias in each layer or even in each neuron. However, it is better if we also have a bias matrix for all the neurons in the hidden layers.
What is supervised and unsupervised tasks in Deep Learning?
Now, this may be a difficult question. There can be a misconception that only deep learning can solve unnecessary learning problems. There is no such thing. Example of supervised and deep learning are:
- Image classification
- Text classification
- Sequence tagging
What are the most used applications of Deep Learning?
Deep learning is used in many different fields today. The most used ones are as follows:
- Sentiment Analysis
- Computer Vision
- Automatic Text Generation
- Object Detection
- Natural Language Processing
- Image Recognition
What are activation functions?
Activation Functions In Deep Learning there are entities that are used to translate input into output parameters.
There are many types of activation functions:
What are the steps involved in perceptual training in deep learning?
There are five main steps that determine a preceptor’s learning.
- Initialize thresholds and weights
- Provide inputs
- Calculate outputs
- Update weights in each step
- Repeat steps 2 to 4
What is the Boltzmann Machine?
This model features a visible input layer and a hidden layer. Only a two-layered neural network that makes statistical decisions about whether a neuron should move or shut down. Nodes are connected across layers, but the two nodes of the same layer are not connected.
What is the Cost Function?
It is also referred to as ‘’loss’’ or ‘’error’’, the cost function is an estimate of networks how well your model is performing. It is used to calculate the output layer during the backpropagation.
What is a CNN?
CNN is a virtual neural network used to analyze images and visuals. These classes of neural can insert a multichannel image and work on it easily.
What is the use of LSTM?
LSTM stands for long short-term memory.it is a type of RNN that is used to sequence a string of data
What are the elements in TensorFlow?
In TensorFlow, users can program three elements:
What is the Bagging and Boosting in Deep Learning?
Bagging is the idea of distributing a dataset and placing it in a random bag for model training.
Boosting is a scenario where incorrect data points are used to force the model to produce incorrect output. It is used to train the model and increase accuracy.
Why should we use Batch Normalization?
Batch Normalization is one of the techniques used in the deep learning algorithms for reducing the training time of users.
Why do RNNs work better with test data?
The key component that distinguishes current neural networks (RNNs) from other models is the addition of a loop to each node. This loop brings the interaction mechanism to the RNN. The addition of a loop is to save the information of the previous node to the next node. This is why RNNs are so much better for sequential data, and since text data is also natural, they are an improvement over ANNs.
How does LSTM solve the vanishing gradient challenge?
The LSTM model is considered a special case of RNNs. The problems of vanishing gradients and exploding gradients we saw earlier disadvantages while using the plain RNN model.
What are autoencoders?
Autoencoders are artificial neural networks that learn without supervision. Here, these networks have the ability to learn automatically by mapping these inputs into relevant outputs.
What is forward propagation?
Forward propagation is the scene where the device is carried by weight to the hidden layer. In each invisible layer, the output of the activation function is calculated until the next layer is processed.
What are Tensors?
In deep learning, tensors are multidimensional rows that are used to represent data. They represent a high level of statistics.
Why the most preferred library in Deep learning is TensorFlow?
TensorFlow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and Torch. TensorFlow supports both CPU and GPU computing devices.
What are Hyperparameters?
A hyperparameter is a parameter whose value is determined before the learning process begins. Determines how the network is trained and the structure of the network.
After reading Deep Learning Interview questions You may also like to read: 20 Basic Machine Learning Interview Questions Answers