SOLUTION: University of the Cumberlands Cognitive Computing Discussion

Attached. Please let me know if you have any questions or need revisions.Running head: COGNITIVE COMPUTING AND DEEP LEARNINGCognitive Computing and Deep LearningStudent NameInstitution of Affiliation1COGNITIVE COMPUTING AND DEEP LEARNING2Question oneIt is an AI characteristic that imitates processes of reasoning by using mathematical data algorithmto learn, as do people (Bini, 2018). The efficiency of standard machine learning techniques islargely dependent on data recognition, while profound learning relies not on statistical analysis. Incontrast to profound learning which automatically acquires the information needed In conventionalartificial intelligence, people need to individually extract and define characteristics that areanalytically or essentially important to the problem and calculate the features in the best formatinto the algorithm to accomplish a given task.Question twoNanotechnology is use of robots for devices to work and guide people. Fuzzified logic is awidespread basic logic in which any idea can have a certain core of truth from 0.0 to 1.0.Expert systems/knowledge- is a form of artificial intelligence (AI) designed to capture expertisefrom expert systems to assist in decision-making?Question threeA number of techniques allow a system to automatically classify the expressions required to detector classify raw data features. With respect to machine education and profound learning, where bothdevelop system features, the operations are assigned to the output layer using interpretationlearning methods.Question fourHard limit: This transfer function causes a neuron to produce a 1 if its net input surpasses, or elseit produces a 0. This allows a neuron to make or classify a decision. Linear function transformsthe neural network into a single layer. A computer program with a linear activation function isCOGNITIVE COMPUTING AND DEEP LEARNING3simply a linear regression model with limited power and ability to manage various parameter datacomplexity parameters. To yield an output that is between 0 and 1, it is recommended thatnonlinear activation functions be used at the output units to portray conditional probability.Positive linear is also referred to as the Rectified Linear Unit input signal, which determines botha unique function and its renunciation when coding.Question fiveMLP is the use of an artificially neural network in large scale to work out a variety of diverseissues, including recognition of pattern and Interpolation. The Perceptron consists of a completelyrelated input layer and output layer. The Multilayer Perceptron (MLPs) breaks this constraint andclassifies non-linearly separable datasets. They do this by studying regression and classificationmodels for complicated datasets using a more rigorous and diverse architecture.Summation weights determines sums of all the input elements joining every processing unit.Function of summation multiplies the weight of each value of input and sums total weighted sumvalues. Activation weights are applied by the neuron to incorporate non-linear properties in thenetwork to determine output from a set of input data.Report on cognitive computingIn dynamic circumstances where the responses may be unclear and vague, the act ofmimicking human thoughts …

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