Question 1

What are the types of Deep Learning Networks?

Accepted Answer

Types of Deep Learning Networks are Feed Forward Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, Restricted Boltzmann Machine, and Autoencoders.

Question 2

How do Machine Learning and Deep Learning differ based on the data type?

Accepted Answer

Machine Learning is best employed for categorical, numerical, time-series, and textual data. On the other hand, Deep Learning models are highly suitable for unstructured data like text, images, sound, or video.

Question 3

What are the various tools used in Deep Learning?

Accepted Answer

Various tools used in Deep Learning are Pandas, Tableau, Matplotlib, and Jupyter notebook. Many people prefer tools like TensorFlow, Pytorch, Microsoft cognitive toolkit, Keras, H2O.ai, neural designer, etc., for implementing Deep Learning.

Question 4

What are the commonly used algorithms in Deep Learning?

Accepted Answer

The most commonly used algorithms in Deep Learning are recurrent neural network (RNN), convolutional neural network (CNN), Deep belief network (DEB), Long Short-Term Memory networks (LSTM), stacked autoencoders, and Deep Boltzmann machine (DBM), etc.

Question 5

How much time does it take to learn Deep Learning?

Accepted Answer

It depends on your expertise level and how well you grasp it. Typically, it takes 6 months to 1.5 years to learn Deep Learning.

Question 6

Why is deep learning called deep?

Accepted Answer

Deep Learning is termed ‘Deep’ due to multiple additional ‘layers’ added to learn from the data. Whenever a deep learning model is learning, it updates the weights across an optimisation function. Essentially, a Layer is a transitional row of ‘Neurons’. A deep learning method sequentially learns categories via its hidden layer architecture. Firstly, it establishes low-level categories like letters and then moves to slightly higher-level classes such as words. Finally, it moves to the higher-level categories like sentences.

Question 7

Why is deep learning significant?

Accepted Answer

Deep learning is a significant component of data science that encompasses statistics and predictive modelling. It is advantageous to data scientists who want to collect, analyse and interpret vast amounts of data. In those cases, deep learning makes the processes easier and faster. Deep learning improves efficiency and productivity by improving response times. Also, it improves relationships and communication with the clients by recording their emotions and adapting the treatment accordingly.

Question 8

Is deep learning used for image classification?

Accepted Answer

With the advent of deep learning, image classification has turned more widespread. The image classification process allows machines to look at an image and allocate a correct label. Image classification with deep learning typically involves convolutional neural networks (CNNs). In CNN's, the nodes in the hidden layers don't always share their outcome with every node in the succeeding layer. Deep learning permits machines to recognise and extract features from the images.

Question 9

Why choose deep learning over machine learning?

Accepted Answer

Traditional machine learning algorithms work on low-end machines, but deep learning algorithms hugely depend on high-end machines. Deep learning algorithms can integrally do many matrix multiplication operations, whereas machine learning algorithms can’t do such operations. Compared to machine learning, deep learning decreases the effort of developing a new feature extractor for each problem.

Question 10

Why is deep learning getting prevalent?

Accepted Answer

One of the most efficient ways to improve the neural network is to train a larger network or input more data. This only works up to some point since you would run out of data or your network becomes so big that it takes a long time to train. But deep learning trains a neural network in less time. Also, it offers faster computation and provides improved algorithms.

Question 11

When was deep learning started?

Accepted Answer

The history of deep learning traces back to 1943, when Walter Pitts and Warren McCulloch developed a computer model depending on the neural networks of the humanistic brain. They used algorithms and mathematics, which they named ‘threshold logic’ to resemble the thought process. Subsequently, deep learning has steadily evolved with two noteworthy breakthroughs in its development.

Question 12

How deep learning models are built on Keras?

Accepted Answer

On Keras, deep learning models are developed using neural networks. The inputs are fed to a neural network which is subsequently processed in the hidden layers through weights adapted during training. Subsequently, the model presents a prediction. The user need not mention what patterns to discover; the neural network learns automatically. The sequential method is the simplest way to develop a model in Keras.

Question 13

How does deep learning extract features?

Accepted Answer

Deep learning is a type of machine learning that can be used to detect features in imagery. It uses a neural network—a computer system designed to work like a human brain—with multiple layers; each layer can extract one or more unique features in the image. Processing is often distributed to perform analysis promptly. Deep learning workflows for feature extraction can be performed directly in ArcGIS Pro, or processing can be distributed using ArcGIS Image Server as a part of ArcGIS Enterprise.

Question 14

What are examples of deep learning?

Accepted Answer

One of the best examples of deep learning is virtual assistants that use a deep learning model to translate the language and speech of human speech. To navigate an autonomous car, for example, a Tesla, one requires a human-like experience and skill. For that, deep learning is useful. This technology is also used in developing a vision for driverless, autonomous cars. Other examples of deep learning are face recognition and chatbots.

Question 15

Will deep learning substitute machine learning?

Accepted Answer

Deep learning will become mainstream like SVM. But, the complexity of deep learning and its requirements for the huge amount of data still must be solved before this technology becomes the foremost choice for machine learning algorithms. Since deep learning positively influences fields like speech recognition, computer vision, natural language processing, etc., it will gradually substitute machine learning in the future.

Question 16

Why is now a perfect time to learn and start using deep learning technology?

Accepted Answer

Deep learning is considered the subsequent logical step in the development of machine learning. This technology equips computers with the ability to understand concepts and human language, similar to how humans do. Being a game-changer, it enables computers to learn how humans learn and can be used for applications ranging from computer vision to computational ecology. Thus, staying abreast with the latest technology trends is the perfect time to learn and use deep learning technology.

Question 17

Is deep learning unsupervised?

Accepted Answer

Deep learning uses supervised learning for object detection and image classification. A neural network can be used as a semi-supervised deep neural network. Autoencoders are neural networks that can be used for image reconstruction and compression; they are self-supervised learning neural networks. Thus, deep learning can be supervised, unsupervised, self-supervised, semi-supervised, or reinforced depending on how the neural network is utilised.

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