The Apache MXNet framework (incubating at the Apache Software Foundation) was developed to enable multiple approaches to the problem of deep learning. One route for reducing the time it takes to train deep learning models involves defining the model and separating it from the algorithm. While this approach speeds training, it can add constraints and complexity because it is hard to update as understanding of the problem improves. Other neural network libraries address this by adding more flexibility, but at the cost of training speed.

Perhaps appropriately, the idea of taking more than one route to tackling a problem is paralleled in other technical aspects of the framework, and in the Apache MXNet community itself. MXNet gives developers the best of both worlds. It provides a concise, easy to understand, dynamic programming interface for defining both the model and the algorithm, without sacrificing training speed.

What is Apache MxNet ?

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It’s highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages.

WHY APACHE MXNET?

virtual void PushSync(Fn exec_fun, Context exec_ctx,
                          std::vector<VarHandle> const& const_vars,
                          std::vector<VarHandle> const& mutate_vars) = 0;

using Fn = std::function<void(RunContext)>;

struct RunContext {
        // stream pointer which could be safely cast to
        // cudaStream_t* type
        void *stream;
    };

using Callback = std::function<void()>;
    using AsyncFn = std::function<void(RunContext, Callback)>;

1. Why Apache MXNet is a powerful open-source deep learning software framework instrument helping developers build, train, and deploy Deep Learning models?

The Apache MXNet is a powerful open-source deep learning software framework instrument helping developers build, train, and deploy Deep Learning models.

2. What are the best deep learning platforms?

There are various deep learning platforms like Torch7, Caffe, Theano, TensorFlow, Keras, Microsoft Cognitive Toolkit, etc.

3. Which is a Gluon toolkit for computer vision powered by MXNet?

GluonCV is a Gluon toolkit for computer vision powered by MXNet.

4. Which of the following are features of GluonCV?

All of the below are features of GluonCV:-

• More than 170+ high quality pretrained models
• Embrace flexible development pattern
• Easy to understand implementations

5. GluonTS is a Gluon toolkit for Probabilistic Time Series Modeling powered by MXNet. State true or flase?

True, GluonTS is a Gluon toolkit for Probabilistic Time Series Modeling powered by MXNet.

6. Which module provides flexible imperative programs for Apache MXNet?

NDArray : It provides flexible imperative programs for Apache MXNet. They are dynamic and asynchronous n-dimensional arrays.

7. Which is a Gluon toolkit for Natural Language Processing (NLP) powered by MXNet?

GluonNLP is a Gluon toolkit for Natural Language Processing (NLP) powered by MXNet.

8. Which API is not thread safe which means that only one thread should make engine API calls at a time?

Push API is not thread safe which means that only one thread should make engine API calls at a time.

9. Operator system module consists of all the operators that define static forward and gradient calculation?

Yes, Operator system module consists of all the operators that define static forward and gradient calculation.

10. What is the difference between Machine Learning and Deep Learning?

Machine Learning forms a subset of Artificial Intelligence, where we use statistics and algorithms to train machines with data, thereby, helping them improve with experience.

Deep Learning is a part of Machine Learning, which involves mimicking the human brain in terms of structures called neurons, thereby, forming neural networks.

11. What is a perceptron?

A perceptron is similar to the actual neuron in the human brain. It receives inputs from various entities and applies functions to these inputs, which transform them to be the output.

A perceptron is mainly used to perform binary classification where it sees an input, computes functions based on the weights of the input, and outputs the required transformation.

12. How is Deep Learning better than Machine Learning?

Machine Learning is powerful in a way that it is sufficient to solve most of the problems. However, Deep Learning gets an upper hand when it comes to working with data that has a large number of dimensions. With data that is large in size, a Deep Learning model can easily work with it as it is built to handle this.

13. What are some of the most used applications of Deep Learning?

Deep Learning is used in a variety of fields today. The most used ones are as follows:

• Sentiment Analysis
• Computer Vision
• Automatic Text Generation
• Object Detection
• Natural Language Processing
• Image Recognition

14. What is the meaning of overfitting?

Overfitting is a very common issue when working with Deep Learning. It is a scenario where the Deep Learning algorithm vigorously hunts through the data to obtain some valid information.

This makes the Deep Learning model pick up noise rather than useful data, causing very high variance and low bias. This makes the model less accurate, and this is an undesirable effect that can be prevented.

15. What are activation functions?

Activation functions are entities in Deep Learning that are used to translate inputs into a usable output parameter. It is a function that decides if a neuron needs activation or not by calculating the weighted sum on it with the bias.

Using an activation function makes the model output to be non-linear. There are many types of activation functions:

ReLU

Softmax

Sigmoid

Linear

Tanh

16. Why is Fourier transform used in Deep Learning?

Fourier transform is an effective package used for analyzing and managing large amounts of data present in a database. It can take in real-time array data and process it quickly. This ensures that high efficiency is maintained and also makes the model more open to processing a variety of signals.

17. What are the steps involved in training a perception in Deep Learning?

There are five main steps that determine the learning of a perceptron:

• Initialize thresholds and weights
• Provide inputs
• Calculate outputs
• Update weights in each step
• Repeat steps 2 to 4

18. What is the use of the loss function?

The loss function is used as a measure of accuracy to see if a neural network has learned accurately from the training data or not. This is done by comparing the training dataset to the testing dataset.

The loss function is a primary measure of the performance of the neural network. In Deep Learning, a good performing network will have a low loss function at all times when training.

19. What are some of the Deep Learning frameworks or tools that you have used?

This question is quite common in a Deep Learning interview. Make sure to answer based on the experience you have with the tools.

However, some of the top Deep Learning frameworks out there today are:

• TensorFlow
• Keras
• PyTorch
• Caffe2
• CNTK
• MXNet
• Theano

20. What is the use of the swish function?

The swish function is a self-gated activation function developed by Google. It is now a popular activation function used by many as Google claims that it outperforms all of the other activation functions in terms of computational efficiency.