Data Science, AI and Vanishing Gradient: Connections and Differences

This assignment explores the intricate relationship between data science, artificial intelligence (AI), and the vanishing gradient problem within the context of deep learning and artificial neural networks (ANN). The analysis begins by defining data science as a field utilizing scientific techniques to extract insights from data, highlighting its role in deep learning. It then examines AI, particularly ANNs, as information-providing models inspired by biological systems, emphasizing their potential to manage complex tasks. The core of the assignment focuses on the vanishing gradient problem, its impact on training and test performance, and its origins in activation functions. The document differentiates between data science and AI, noting that data science focuses on deep learning and information extraction, while AI provides automated systems. The assignment emphasizes that ineffective learning and poor training are the major factors that lead to vanishing gradient. Therefore, it is suggested that developers should provide complete training and include effective learning algorithms while implementing AI-based systems.

DATA MINING 0
Data Mining

DATA MINING 1
Data science and artificial intelligence
Data science represented the entire procedure of determining meaning in data and
deep learning is a kind of technology utilized in machine learning. Data science is a
different sector that utilizes scientific techniques, procedures, and programs to extract
knowledge and insights from numerous structural and formless data.
Zhu, & Zheng, (2018) reported that data science plays a significant character in deep
learning where it helps to obtain effective information and data from gathered structural
and unstructured data. Artificial intelligence is a growing technology that helps to connect
humans with the computing networks and it is completely related to deep learning and test
performance where individuals can obtain effective information and manage workability.
It is found that artificial intelligence has the potential to manage complex tasks
effectively and provide effective learning to the consumers by which companies can
enhance business performance. An artificial neural network is a part of AI technology
which is defined as an information providing model that is generally inspired by the
biological nervous system including the brain.
Such kind of networks helps to implement effective programs by which individuals
can handle vanishing gradient related problems in an appropriate manner. It is determined
that artificial intelligence and data science both are interconnected to effective learning and
help to handle multiple tasks at a time by using machine learning programs.

DATA MINING 2
Ghahramani, (2015) determined that vanishing gradient is a kind of problem
examined in training center artificial neural networks. Such kind of problem makes it really
complex to learn and tune the elements of earlier layers in the computing networks.
The presence of such problems in the artificial network can impact on the test
performance and produce barriers in deep learning. It is a kind of problem with gradient-
based learning techniques caused by several activation functions.
It is found that data science is different from artificial intelligence as data science
focuses on deep learning and provides effective information but AI technology help to
provide automated systems by which consumers can obtain effective information from
gathered datasets. Mikhaylov, Esteve, & Campion, (2018) highlighted that data science
contains various underlying data operations while AI and ANN networks are limited to the
implementation of deep and machine learning programs.
The major difference between data science and AI is that data science contains
structured and unstructured data while AI standardized in the form of vectors and
embedding. Therefore, it is reported that both data science and AI are different but can be
used in deep learning and help to enhance the overall performance of the systems.
However, ineffective learning and poor training are major factors that lead to vanishing
gradient and other problems in artificial intelligence and data science.
So, it is suggested that developers should provide complete training and include
effective learning algorithms while implementing AI-based systems (Van, & Bohte, 2017).
Vanishing gradient problem can be addressed by using effective functions that do not have
the property of squashing the applied signals into small regions.

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References
Ghahramani, Z. (2015). Probabilistic machine learning and artificial
intelligence. Nature, 521(7553), 452-459.
Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector:
opportunities and challenges of cross-sector collaboration. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and Engineering
Sciences, 376(2128), 20170357.
Van Gerven, M., & Bohte, S. (2017). Artificial neural networks as models of neural
information processing. Frontiers in Computational Neuroscience, 11(6), 114.
Zhu, L., & Zheng, W. J. (2018). Informatics, data science, and artificial
intelligence. Jama, 320(11), 1103-1104.