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Advantages and Disadvantages of Deep Learning

  • Ashesh Anand
  • Aug 01, 2022
Advantages and Disadvantages of Deep Learning title banner

You have probably seen the growth of cutting-edge ideas like deep learning over the past few years, as well as some major corporations adopting it. It makes sense to question why deep learning has drawn the attention of business owners all across the world. 

 

In this article, we'll examine deep learning in more detail and attempt to identify the major factors contributing to its rising popularity.


 

Deep Learning: What Is It?

 

Deep learning is a subset of machine learning (ML), which processes data according to a predetermined logical framework in an effort to uncover correlations and patterns. Deep learning, also known as deep neural networks, employs a number of hidden layers in the neural network as compared to conventional neural networks, which have a limited number of hidden layers.

 

To get an accurate result, deep learning algorithms map inputs to previously learnt data. This technology's underlying idea is extremely similar to how human brains work (biological neural networks). To get a conclusion, we contrast fresh information with previously discovered data.

 

Large collections of labeled data and neural network topologies that automate feature learning without the need for manual extraction are used to train deep learning models.

 

Difference Between Deep Learning and Machine Learning

 

While both technologies employ data for feature learning, deep learning's capacity to scale with data distinguishes it significantly from machine learning. 

 

After training on big data sets, ML systems typically reach a performance plateau before diminishing returns set in. However, deep learning models perform better as the size of the training datasets grows.

 

While machine learning requires data scientists or users to extract and build features, deep learning automatically performs feature extraction and modeling processes after data training. Deep learning models can tackle problems that traditional machine learning models cannot.

The image shows the differences between Deep Learning and Machine Learning on the basis of Data Requirement, Accuracy, Training Time, Hardware Dependency and Hyperparameter Tuning

Deep Learning vs Machine Learning

 

Also Read | How are Machine Learning and Deep Learning Different?

 

How Does Deep Learning Operate?

 

Training and inferring are the two primary stages of a deep machine learning process. Consider the training phase as a process of classifying massive amounts of data and identifying their shared traits. In order to draw the appropriate conclusions the next time it encounters data of a similar nature, the system compares and memorizes these traits.

 

The stages of a deep learning training process are as follows:

 

  1.  ANNs pose a series of binary true/false queries.

  2.  Extraction of numbers from blocks of data.

  3.  Sorting data into categories based on the responses.

  4.  Data Labeling.

 

The deep learning AI draws conclusions and labels new, uncovered data during the inferring phase using its prior knowledge.

 

Also Read | A Guide to Transfer Learning in Deep Learning


 

Advantages of Deep Learning

 

You might wonder why so many major IT companies are gradually implementing deep learning. We must examine the benefits of a deep learning technique in order to comprehend the cause. These are the top eight benefits of employing deep learning :

 

  1. According to Gartner research, a significant portion of an organization's data is unstructured because the majority of it exists in many types of forms, such as images, texts, and so on. Unstructured data is underutilized because it is challenging for the bulk of machine learning algorithms to interpret it. This is where deep learning excels.

 

Deep learning algorithms can be trained using a variety of data types and still produce insights that are pertinent to the training's objectives. For example, you can utilize deep learning algorithms to find any connections between market research, social media activity, and more to forecast future stock values of a specific company.

 

  1. Feature engineering is a vital task in machine learning since it increases accuracy, and occasionally the procedure can call for expertise of a specific problem's domain. Utilizing a deep learning approach has many benefits, one of which is its independence in performing feature engineering.

 

In this method, an algorithm searches the data to find traits that correlate and then combines them to encourage faster learning without being specifically instructed to do so. This skill enables data scientists to significantly reduce their workload.

 

  1. Humans occasionally make careless errors when they are hungry or exhausted. This isn't the case with neural networks, though. Once correctly trained, a deep learning model can complete thousands of mundane, repetitive activities in a small fraction of the time it would take a human to do them. 

 

Additionally, the work's quality never declines unless the training data includes raw data that doesn't accurately reflect the issue you're seeking to solve.

 

  1. Recalls are quite expensive, and in some sectors they can result in direct expenses to an organization of millions of dollars. Deep learning can be used to detect subjective flaws that are challenging to train, such as tiny typos on product labels.

 

Deep learning models can also find flaws that would be challenging to find otherwise. Deep learning can take into consideration these variances and learn useful features to strengthen inspections when consistent images become difficult for various reasons.

 

  1. The process of data labeling can be costly and time-consuming. The use of well-labeled data is no longer necessary when using a deep learning approach because the algorithms are excellent at learning without any rules. This sort of learning is much more effective than other types of machine learning approaches.

 

  1.  Although most data scientists have learnt to regulate the learning process to concentrate on what's essential to them, it is robust enough to grasp and apply novel data. By letting you manage the learning but not the statistical modeling, deep learning takes advantage of this.

 

  1. Rather than teaching the system how to learn, it lets us teach a specific task. To train a certain model, we can utilize a variety of instances or a very basic training set and just ask it to pick things up.

 

  1.  It can evolve into any system. It can be used for a variety of purposes, such as simple facial recognition or image reconstruction. It can be done with a lot of weights or very few weights. It could be linear or not.


 

Disadvantages of Deep Learning

 

Instead of employing human abstract thinking, deep learning models it (or at least makes an attempt to approximate it). Despite all of its advantages, there are several disadvantages of deep learning :

 

  1. Deep learning relies on data analysis to build its training process. However, the amount of time needed to ensure an effective training process is limited by the fast-moving and streaming input data. 

 

Data scientists must therefore modify their deep learning algorithms so that they can take advantage of the fact that neural networks can process massive volumes of continuous incoming data.

 

  1. Deep learning software's inability to explain its reasoning for reaching a certain conclusion is a significant drawback. You cannot follow an algorithm, unlike in the case of conventional machine learning, to determine why your system determined that a photo was of a cat and not a dog. You must modify the entire algorithm in order to fix faults in Deep Learning algorithms.

 

  1. Deep learning is a technology that uses a lot of resources. To train the models, it necessitates more potent GPUs, high-performance graphics processing units, enormous amounts of storage, etc. Furthermore, compared to conventional machine learning, this approach requires more time to train.

 

  1.  It is far more difficult to compare its results to those of handcrafted techniques. It is possible to extend deep learning to higher-dimensional regions using a different strategy known as "deep learning by gradient descent."

 

  1.  It is quite challenging to evaluate its performance in real-world applications because one application can differ substantially from the others and testing methods for analysis, validation, and scalability can be highly different.

 

  1.  It is quite challenging to comprehend. Deep learning is the next development in machine learning as a result. By learning more about the world, it enables machines to become more advanced. From this knowledge, they can then extrapolate a generalization that they can later use to solve a different problem.

 

Despite all of its difficulties, deep learning uncovers new, better ways to analyze unstructured large data for those who intend to use it. In fact, utilizing deep learning for data processing activities can have a positive impact on enterprises. However, the real question is not whether this technology is helpful but rather how businesses may use it in their projects to enhance data processing.

 

Also Read | Best Deep Learning Techniques


 

Applications for Deep Learning

 

  1. Virtual Assistants

 

Virtual assistants are web-based programmes that take user commands in natural language and carry them out. Virtual assistants like Amazon Alexa, Cortana, Siri, and Google Assistant are common examples. 

 

To operate to their greatest potential, they require computers with an internet connection. Based on prior interactions and the usage of Deep Learning algorithms, the assistant generally offers a better user experience each time a command is given to it.

 

  1. Chatbots

 

Chatbots can quickly fix consumer issues. A chatbot is a text- or text-to-speech-based AI application for online communication. It has the ability to interact with people and carry out human-like tasks. 

 

Chatbots are frequently employed in customer service, social media marketing, and instant messaging clients. Inputs from users are met with automated responses. To produce various forms of reactions, it employs machine learning and deep learning algorithms.

 

  1. Medical Care

 

Deep learning is being used in the healthcare industry. Deep Learning has made it feasible to detect diseases and make diagnoses with computers. Through the use of medical imaging, it is frequently employed for medical research, medication discovery, and the identification of serious illnesses like cancer and diabetic retinopathy.

 

  1. Entertainment

 

To improve their customers' experiences, businesses like Netflix, Amazon, YouTube, and Spotify provide relevant movie, song, and video suggestions. Deep Learning is entirely to blame for this.

 

Online streaming businesses make recommendations based on a person's surfing history, interests, and activity to assist them in making product and service decisions. Deep learning methods are also used to automatically create subtitles and add sound to silent films.

 

  1. News Aggregation and Detection of Fake News

 

Using deep learning, you can tailor news to the personas of your readers. A reader's own tastes as well as social, regional, and economic factors can be used to collect and filter news material. The development of classifiers that can detect fake and false news and remove it from your feed is assisted by neural networks. They also alert you to potential privacy violations.

 

  1. Making Songs

 

The notes, structures, and patterns of music can be taught to a machine, which can then begin to compose music on its own. Raw audio can be created using generative models powered by deep learning, such as WaveNet. 

 

The Long Short Term Memory Network aids in the automated generation of music. Computer-assisted musicology makes use of the Python Music21 toolkit. By imparting basic knowledge of music theory, creating musical samples, and researching music, we may train a system to create music.

 

Also Read | Music Composition Using Deep Learning


 

Final Reflections

 

In light of the aforementioned benefits of adopting deep learning techniques, it is safe to say that deep learning will undoubtedly have an impact on the development of future high-end technologies like Advanced System Architecture and the Internet of Things. More beneficial contributions to the greater corporate world of linked and smart products and services are to be expected.
 

Deep learning is no longer just a trend; it is now swiftly evolving into a vital technology that is being progressively embraced by a variety of enterprises across numerous industries.

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