Deep Learning - Overview, Practical Examples, Popular Algorithms

This blog covers the overview of Deep learning terms and technologies, pros and cons, basic functionality, and practical examples. Further added, how it is different from machine learning and artificial intelligence, ways to train deep learning methods, utilized algorithms, and lastly the key takeaways.

 

Topics Covered

 

1.   What is Deep Learning?

2.   Advantages and Disadvantages of Deep Learning

3.   Practical examples of Deep Learning

4.   Difference between Machine Learning and Deep Learning

5.   How deep learning works?

6.   Popular algorithms in Deep Learning

7.   Conclusion

 

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. It is a prime technology behind the concept of virtual assistants, facial recognition, driverless cars, etc. The working of deep learning involves training the data and learning from the experiences.

 

The learning procedure is called ‘Deep’, as with every passing minute the neural networks rapidly discovering the new levels of data. Each time data is trained, it focuses on enhancing the performance. With the increasing depth of the data, this training performance and deep learning capabilities have been improved drastically. 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.
• Its ability to 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.

 

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 to the data sets.

 

 

Practical Examples of Deep Learning

 

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

 

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, 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.

 

 

Difference between Machine learning and Deep learning

 

Although Deep learning is the one category of machine learning and artificial intelligence, still there are many bases to differentiate between them. The primary goal is to provide an optimized algorithm to increase the efficiency in working. The differences would be best explained through tabular form, detailing overworking mechanism, management, output, practical real-life examples and data points including utilized algorithms for respective algorithms.

 

 

 

Attributes	Deep Learning	Machine learning
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.
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.
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 variable under the multiple datasets.
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.
Data points	Data points are used for analysis usually numbered in millions.	Data points are used for analysis usually numbered in thousands.
Training time	Considering larger parameters, deep learning takes a long time for training.	Machine learning algorithms usually takes less time for analysis, ranging from a few minutes to hours.
Considered algorithms	It makes use of neural networks.	It utilizes algorithms like linear regression, random forest, and KNN.
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.

 

 

How Deep Learning Works?

 

Deep learning algorithms utilizes supervised and unsupervised learning algorithms to train the outputs through the delivered inputs. The below circles are represented as neurons that are interconnected. The neurons are classified into three different hierarchy 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.

 

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.

 

Popular 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

 

Conclusion

 

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 make the 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.

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