Deep Learning And How It Hlep In Business Essay

Question:

Discuss about the Deep Learning And How It Is Implemented In The Business.

Answer:

My Contribution

Firstly, I must mention that I did not have adequate idea about deep learning and how it is implemented in the business. However, when preparing deep learning activities, I have gone through few journals about deep learning or machine learning, which helps me to make our group project accordingly. I have derived an intensive insight about deep learning and my ideas helped my group members to work on the project. Initially, my group members thought that deep learning should be applied to develop the research and development department of the chosen organization. However, my understanding about this context helped my group members to learn that deep learning can be applied across all business functions in the originations. To help my group members, I have written introductory part of the assignment, which helps the members to understand how the project should go forward and meet its purpose. I wrote a complete overview of deep learning and its significance in business. Based on my overview, my group members chose their individual section to work on –such as one of the members took the responsibility of collecting journals on the application of deep learning in business. Likewise, I took the responsibility of finding information regarding the emerging trends of deep learning and their potential application.

While preparing the review of the literature, my group members gave the idea of collecting information about machine learning and presenting them sequentially on the report but I refused t idea. I explained that literature review should be critical and we have to make different argumentative points considering the ideas and opinion of scholars on this context. However, making a critical review is difficult as several evidences are required. Thus, to overcome this challenge, I helped my group members to collect more journals that have authentic information and evidences of allocation of deep learning. We collected those journals and identified the result and discussion provided by the scholar and eventually we present them in the report in an argumentative way. Another challenge we found was finding information about how deep leaning is applied to the business traversing each function of the business; hence my contribution was, finding the internal elements associated with deep learning such as KPI tracking and presenting how visualization of data is backing up the deep learning system without any delay and flaws. Hence, I also had to find real-world examples of deep learning application such as the Invoca –an organization using the deep approaches to enhance their business process and functions.

Similarly, another significant responsibility that I undertook was finding the evidence about how deep learning can help to gain insight about consumer behavior in the market. Hence, I provided the data that with the help of cloud based business platforms; the business can obtain actionable business insight where an element of deep learning such as Artificial Intelligence plays a big role. We gained the insight that but using deep learning approaches, the organizations can get more skilled or trained employees, which is usually reflected on organization’s overall performance. In the literature, we were supposed to show the implementation or adoption of deep learning in different industries.

Therefore, I have collected the data of financial sector where the organizations have applied deep learning approaches to increase their client satisfaction. Likewise, I have collected evidences of healthcare industry, where the organization have applied to deep learning to gain insight about how medication can be developed with technology. Thereby, I must mention that my contribution in preparing the group project was significant because the evidences that I collected for review was authentic which further helped us to gain the result that deep learning is highly effective and it remains as great toll to identify the changing market environment trends.

Recommendation

The current scenario of Neto Ecommerce indicates that the organization has developed its own retail and wholesale platforms and provides a complete solution for e-commerce, point of sales, inventory and fulfillment. However, as the organization is dealing with several business functions and operation it requires a large or extending workforce, which would help the firms to complete all its operation on time. Thereby, instead of investing on hiring new workforce, the organizations can incorporate deep learning in its business functions, which would help them to speed up the organization operation and enhance client satisfaction rate. In addition, to enhance the current position such as the customer satisfaction rate, the organization can use any of the deep learning elements such as AI technology in its production system. In addition to this scenario also helps to understand the organization needs to enhance customer base and to do this the firm needs filtered consumer data of each regions where the organization is currently performing the operation or it wishes to perform in the future. Hence, with the help of deep learning approaches, Neto can install a machine is coded with historical data and information of customers.

Hence, these deep learning approaches should be applied in a manner for example, that the system shows the areas in which the consumers have a changing needs. For example, if an organization members looks for market information of Texas, the system provide the same and a similar market with different size and structure. In addition to this, the organization spend a huge amount of money in market analysis to know customer needs. Nonetheless, if the organization uses deep learning method to identify customer needs, the proposed system will help the organization to learn the similar needs of the customer or the future needs, as AI system is designed in a way that it creates an automatic calculation and provide response. For example, if the market analysts want to learn which beauty product that customers mostly want, the system provides result and a similar result such as recommended skin care product.

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