The field of artificial intelligence is basically when machines can perform tasks that normally require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without any human involvement.
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.
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.
(Speaking of Deep Learning you can also sneak a look at our other blogs on the topic)
Along with the ample amount of benefits, threats also surfaces due to the unexplored capabilities of deep learning. Deep learning utilizes supervised, semisupervised and unsupervised learning to train from the data representations. The functionality of deep learning relies on the below points:
With the increasing popularity, deep learning also has a handful of threats that needs to be addressed:
This section discusses, the focus and problems that surround the working of Deep learning:
Examples of 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 reallife examples and data points including utilized algorithms for respective algorithms.
Deep Learning as subset of ML and AI
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 subbranch 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 selfdirected 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. 
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.
Working network of Deep Learning
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:
The popular algorithms that have been utilized to create a strong foundation for deep learning algorithms are:
The greater the experience of deeplearning algorithms, the more effective they become. As the technology progresses over the years, it has the potential to become extraordinary.
With this, the blog on the basics of Deep learning is summed up.
For more blogs in Analytics and new technologies do read Analytics Steps.
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