Deep Learning - Overview, Practical Examples, Popular Algorithms

The field of AI is something where machines can perform tasks that normally requires human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without any human involvement. 

 

Deep learning is the subfield of machine learning, supporting algorithms that are inspired by the structure and function of the human brain, and named as artificial neural networks.

 

 

Topics Covered

 

1. What is Deep Learning?

2. Advantages and Disadvantages of Deep Learning

3. Examples of Deep Learning

4. Machine Learning vs Deep Learning

5. Working of Deep Learning 

6. Top algorithms in Deep Learning

 

What is Deep Learning?

 

Deep learning is the one category of machine learning that emphasizes training the computer about the basic instincts of human beings. 

 

In deep learning, a computer algorithm learns to perform classification tasks directly on complex data in the form of images, text, or sound. These algorithms can accomplish state-of-the-art (SOTA) accuracy, and even sometimes surpassing human-level performance. They are trained with the large set of labeled data and neural network architectures, involving many layers. Moreover;

 

1. Deep Learning is a prime technology behind the technology such as virtual assistants, facial recognition, driverless cars, etc. 

2. The working of deep learning involves training the data and learning from the experiences. 

3. The learning procedure is called ‘Deep’, as with every passing minute the neural networks rapidly discover the new levels of data. Each time data is trained, it focuses on enhancing the performance. 

4. With the increasing depth of the data, this training performance and deep learning capabilities have been improved drastically, and this is because it is broadly adopted by data experts.

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Why to choose deep learning, Source

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Along with the ample amount of benefits, threats also surfaces due to the unexplored capabilities of deep learning.

 

Deep learning utilizes supervised, semi-supervised and unsupervised learning to train from the data representations. The functionality of deep learning relies on the below points:

 

• It imitates the functionality of a human brain for managing the data and forming the patterns for referring it in decision making.

• The trained dataset can be interconnected, diverse and complex in nature.

• The larger the data set, the more efficient the training that directly impacts the decision making.

 

 

Advantages of Deep Learning

 

• Ability to generate new features from the limited available training data sets. 

• Can work on unsupervised learning techniques helps in generating actionable and reliable task outcomes. 

• It reduces the time required for feature engineering, one of the tasks that requires major time in practicing machine learning. 

• With continuous training, its architecture has become adaptive to change and is able to work on diverse problems.

 

(Must read: Machine learning Applications)

 

 

Disadvantages of Deep Learning

 

With the increasing popularity, deep learning also has a handful of threats that needs to be addressed:

 

• The complete training process relies on the continuous flow of the data, which decreases the scope for improvement in the training process.

• The cost of computational training significantly increases with an increase in the number of datasets.

• Lack of transparency in fault revision. No intermediate steps to provide the arguments for a certain fault. In order to resolve the issue, a complete algorithm gets revised. 

• Need for expensive resources, high-speed processing units and powerful GPU’s for training the data sets.

 

 

Examples of Deep Learning

 

This section discusses, the focus and problems that surround the working of Deep learning:

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Examples of Deep Learning

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1. Virtual Assistants:

 

The core functionality that requires translating the speech and language of the human’s speech, is deep learning. The common examples of virtual assistants are Cortana, Siri, and Alexa.

 

2. Vision for Driverless, Autonomous Cars: 

 

In order to navigate an autonomous car, say, a Tesla, one needs a human-like experience and expertise. 

 

To understand the scenarios of roads, the working of signals, pedestrians, significances of different signs, speed limits and many more situations like these, a large amount of real data is required. 

 

With the large data, the efficiency of the algorithms will be improved which will subsequently increase the decision-making flow.

 

3. Service and Chat Bots:

 

The continuous interaction of chatbots with human beings for providing customer services requires strong responses. 

 

To respond in a helpful manner with all the tricky questions and apt response, deep learning is required for training algorithms.

 

4. Translations: 

 

Translating the speech automatically in multiple languages requires deep learning supervision. This is a helpful mechanism for tourists, travelers, and government officials. 

 

5. Facial Recognition: 

 

Facial recognition has many features from being used in the security to the tagging mechanism/feature used on Facebook. 

 

Along with the importance, it has its fair share of issues as well. For example, to recognize the same person with weight gain, weight loss, beard, without a beard, new hairstyles, etc. 

 

6. Shopping and Entertainment: 

 

All the shopping applications like Amazon and Myntra and entertainment applications like Amazon Prime and Netflix store your data and buying habits to show the suggestions for future buying and watching. 

 

It always comes as a title “You may like to watch/buy”. The more data is inputted in the Deep learning algorithm, the more efficient it becomes in decision making. 

 

7. Pharmaceuticals: 

 

Customizing medicines based on the particular genome and diseases. Deep learning has widened the scope of such applications and has gained the attention of the largest pharmaceutical companies. 

 

Besides that, other deep learning applications are fraud detection, virtual recognition, healthcare, entertainment and many more.

 

 

Machine learning vs Deep learning

 

Although Deep learning is the one category of machine learning and artificial intelligence, still there are many bases to differentiate between them. 

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Deep learning ⊂ ML ⊂ AI

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The primary goal is to provide an optimized algorithm to increase the efficiency in working. The differences would be best explained through tabular form, as given below;

 

S.No	Attributes	Deep Learning	Machine learning
1	Definition	It is a subset of machine learning with the constant focus on achieving greater flexibility through considering the whole world as a nested hierarchy of concepts.	It is a sub-branch of Artificial intelligence. It allows the machines to train with diverse datasets and predict based on their experiences.
2	Working mechanism	Neural networks help in interpreting the features of data and their relationships in which important information is processed through multiple stages of processing the data.	It utilizes automated algorithms to predict the decisions for the future and modeling of functions based on the data fed to it.
3	Management	All the algorithms are self-directed after the implementations for result fetching and data analysis.	All the analysis is managed by analysts to evaluate different variables under the multiple datasets using ML algorithms.
4	Practical examples	Practical examples are virtual assistants, shopping & entertainment, facial recognition, translations, pharmaceuticals, and vision for driverless vehicles.	Practical examples are speech recognition, medical diagnosis, statistical arbitrage, classification, prediction, and extraction.
5	Data points	Data points are used for analysis usually numbered in millions.	Data points are used for analysis usually numbered in thousands.
6	Training time	Considering larger parameters, deep learning takes a long time for training.	Machine learning algorithms usually take less time for analysis, ranging from a few minutes to hours.
7	Considered algorithms	It makes use of neural networks.	It utilizes algorithms like multiple linear regression, random forest, and KNN.
8	Output	The output is usually diverse like a score, an element, classification, or simply a text.	The output for this algorithm is usually a numeric value like a classification.

 

A distinguished explanation is provided in the link, Deep Learning vs Machine Learning 

 

 

Working of Deep Learning

 

Deep learning algorithms utilizes supervised and unsupervised learning algorithms to train the outputs through the delivered inputs. 

 

See the image below, these circles represent neurons that are interconnected. The neurons are classified into three different hierarchies of layers termed as Input, Hidden and Output Layers. 

 

• The first neuron layer i.e. input layer receives the input data and passes it to the first hidden layer. 

• The hidden layers perform the computations on the received data. The biggest challenge under neural networks creation is to decide the number of neurons and a number of hidden layers. 

• Finally, the output layer produces the required output.

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Working network of Deep Learning

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This is the basic flow of working. Now, comes the point where the method of computation is explained. 

 

Every connection between the neurons consists of weights, it denotes the significance of the input values. In order to standardize the outputs, an activation function is used. 

 

For training the network, two important measures are considered. The first is to create a large data set and the second is large computational power. The ‘Deep’ in deep learning signifies the number of hidden layers the model is using to train the data set. 

 

Working of Deep learning can be summed up in four final points:

 

1. ANN asks a combination of binary True/False queries.

2. Extracting numeric values from blocks of data.

3. Sorting the data as per the received answers.

4. A final point is marking/labeling the data.

 

 

Top Algorithms in Deep Learning

 

The popular algorithms that have been utilized to create a strong foundation for deep learning algorithms are:

 

1. KNN (K - nearest neighbor) method

2. Artificial Neural Network (ANN)

3. Convolutional Neural Network (CNN)

4. Recurrent Neural Network (RNN)

5. Deep Neural Network (DNN)

6. Deep Belief Network (DBN)

7. Back Propagation

8. Stochastic Gradient Descent

 

 

Summary

 

With this, the blog on the basics of Deep learning is summed up. 

 

• Deep learning is a sub-branch of AI and ML that follow the workings of the human brain for processing the datasets and making efficient decision making.

• Deep learning utilizes both structured and unstructured data for training.

• Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.

 

(Suggested articles: Examples of AI)

 

The greater the experience of deep-learning algorithms, the more effective they become. As the technology progresses over the years, it has the potential to become extraordinary.