Deep Learning Tutorial

 Deep Learning Tutorial

In this tutorial, we will discuss the Deep Learning, and as the part of the deep learning, we will learn the following things that are as follows:

1)     Working of Deep Learning.

2)     Hardware Requirement for the Deep Learning.

3)     Methods related to Deep Learning.

4)     Various fields where deep learning is used.

5)     Limitations and challenges of Deep Learning.

 

What do you mean by Deep Learning?

Deep Learning is considered one of the most common concepts in the modern computer world.

“Deep learning is an important part (subset) of Machine Learning. It is a neural network encapsulated with three or more layers where the respective neural network effectively simulates the behaviour related to the human brain. The neural network with a combination of single-layer can also help in the predictions (approximate). The additional hidden layers can also help to optimize and refine the accuracy to a greater extent.”

Furthermore, Deep Learning also drives many useful artificial intelligence (AI) services and applications that, in turn, help in improving automation in performing analytical and also physical tasks without any intervention by normal human beings. The deep learning technology lies behind every day-to-day life products and the services that primarily include the following:

·        TV remotes with voice-enabled technology.

·        Effective Fraud detection related to the debit card or the credit card up to a greater extent.

·        Self-driving cars.

·        And many more etc.

To better understand deep learning, one can imagine the beautiful example of the toddler in which the first word for the toddler is none other than the "dog". Hence, the toddler gets insight or learns what a dog is and is not a dog. This could be done by referring (pointing) to the object and calling it a dog. But the parents say, "Yes, that object which toddler is pointing is a dog", or the parent could say:" No, the object that the toddler was pointing was not a dog". 

Moreover, as the toddler continues pointing the particular objects, the toddler will gain more knowledge about the objects, so in each level of the hierarchy of pointing, the toddler learns the entire basic feature of the object. Without any problem, he can identify it like a dog.

Working of Deep Learning

As it was known, the computer system or the computer program that uses deep learning goes through the steps or the process which are quite similar to the learning process of the toddler to identify the object as discussed above. And each of the algorithms used in the hierarchy eventually applies a non-linear form of the transformation to its input. It uses what it learns to create the model to be the output effectively.

The iterations of the hierarchy continue until and unless the desired output is not achieved

Hardware Requirements for the Deep Learning

Deep Learning eventually requires a tremendous amount of computing power. Therefore, the high-performance related graphical processing units (GPUs) can be considered ideal for deep learning as they can handle many calculations with the availability of huge memory allocation. The disadvantage of using GPUs (Graphical processing units) for deep learning is that their maintenance is much costlier than the other hardware devices.  

Deep learning methods

The various methods that can be used effectively to create a very strong form of the deep learning models are as follows:

·        Transfer Learning.

·        Training from scratch.

·        Dropout.

Transfer Learning: It is considered to be the most important method that can be used for deep learning, as the transfer learning process involves perfecting the previously trained model; it also requires an interface for the internals of the already existing network. 

In this, the respective users first feed the particular feeding existing network new data containing unknown classifications from the previous ones. And once the task of adjustments to the network is made, new tasks can be performed with the zest and zeal to categorize abilities efficiently.

Training from Scratch: It is considered to be another most important method that can be used to learn deep learning, as these effective methods require the developer to make a collection of the data set in a large quantity and also to make configuration of the network architecture which are used to learn the useful features and the model.

Moreover, this method is quite popular for new applications as well as applications that have a large number of output categories. And also less popular because it requires a huge and excessive amount of data that eventually takes lots of training.

Dropout: The most important method, other than transfer learning and training from scratch, is Dropout, which can be used for learning deep learning. The Dropout method attempts to solve the problem related to the over lifting in the particular networks with a large amount of the parameters with the help of randomly dropping the units and their connections from the respective neural networks during the training process.

  Various fields

The various important fields where the deep learning is mostly used are as follows:

·       In the effective generation of the text.

·       Used in the field of the Military as well as the Aerospace.

·       In the field of Industrial Automation.

·       For the research in the medical field.

·       For a deep vision of the computer system.

·       To provide a better customer experience to the particular customer.

·       And many more etc.

Limitations and the challenges

The various limitations and the challenges that are affiliated with the Deep Learning are as follows:

1.      Deep learning makes the requirement of huge amounts of data. Moreover, using a powerful and accurate model will require more parameters and a bulge amount of data to meet the defined models.

2.      And Another limitation and challenge of deep learning are that once the trained learning model gets inflexible and cannot handle multitasking conveniently. And now, they can deliver efficient and accurate solutions but are restricted to only one specific problem. If an individual wants to solve the same problem, he must retrain the system.

3.      And many more etc.