WK2_Disk_R

Disk R 

Discussion 1:

Artificial intelligence can be defined as the act of training a machine without providing the proper instructions for the training for any specific task. Such as the machines being used for the facial detection will not be provided information about the way to detect the facial structure and the way to differentiate between different facial structures, but will be provided with a set of information. In this case, this set will be a set of images of a number of people. Machine will automatically scan these images and train itself for the task.

There are two different types of information which can be provided to the machine. This can be labelled and non-labelled data. In labelled data, a specific information about the image will also be provided along with the image and the machine will train itself based on the description provided. In case of non-labelled data, the machine will not be provided information about the data set provided and the machine will have to train itself from the scratch based on the information provided.

The major process involved in AI is the production of a neural network. This neural network is then provided with the data set, this data set is used by the neural network to train itself. Once trained, a sample run is done on the test images to check the accuracy of the working of the network and lastly the network can be used on the real life information for performing tasks.

The major difference between the two fields of deep learning and machines learning is based on the type of information being provided to the machine. Such as, in machine learning these algorithms require structured information for the training process but the deep learning algorithms majorly are based on the ANN (Artificial Neural Networks).

References:

Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer, ISBN 978-0-387-31073-2

Machine learning and pattern recognition “can be viewed as two facets of the same field.”[3]:vii

Friedman, Jerome H. (1998). “Data Mining and Statistics: What’s the connection?”. Computing Science and Statistics. 29 (1): 3–9.

Discussion 2:

With several advantages of utilizing information technology enabled processing capabilities, there are several advances in the way processes are conducted that can provide unique advantages to the organizations to improve their business prospects and develop new opportunities and avenues for exploring their business opportunities. The introduction of business intelligence and Data Mining capabilities allows systematic approaches to be conducted in evaluating the information available and to make informed decisions to improve the current situation and develop better prospects for the future.

Machine learning is a process to develop autonomous processing capabilities that can handle the incoming traffic efficiently to identify specific conditions in the information to respond to certain conditions evaluated to match predefined criteria. The process has allowed improved decision-making capabilities at a lesser time and develops a better understanding of the process with a consistent response to predefined conditions. It is the ability to evaluate the information from the pile of information available which is dynamic and unstructured to some extent (Dean, 2014).

Developing a better understanding of the available information to create useful meaning out of the information available requires a deeper understanding of the information to be developed and an autonomous process that can help improve its understanding by developing neural networks allows meaningful understanding to be created and processing of the information becomes efficient, accurate, and timely with the help of the understanding developed and improved. Deep learning is a process of creating the accurate meaning of the information flowing which includes sentimental analysis, word processing, the grouping of information, developing appropriate understanding by breaking the information into smaller pieces that can be used by the process to develop understanding and respond, improve the accuracy of the response that the additional learning capabilities (Nikhil Buduma, 2017)

References:

Dean, J. (2014). Big Data, Data Mining, and Machine Learning: Value Creation for Business. Wiley.

Nikhil Buduma, N. L. (2017). Fundamentals of Deep Learning: Designing Next-Generation Machine. Oreilly.