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management and artificial intelligence

Questions and Answers of Management And Artificial Intelligence

What is the PySimpleGUI library?
What is face swap, and how does it work?
What is the DeepFace library?
How does emotion detection work?
How does gender detection work?
How does age detection work?
What are face landmarks?
What is face recognition? How many steps are there in face recognition?
What is face detection?
What is the Fractalius effect?
What is a Gabor filter? What are the effects of different wavelengths(or frequencies) and different orientations on Gabor wavelets?
What is the image super resolution?
What is the PySimpleGUI library?
What is meant by image stitching, template matching, region of interest, and photo inpainting in the context of image processing?
What is the Streamlit library?
What is federated learning?
Use a suitable table to compare the characteristic features of X-ray, MRI, and CT imaging.
What are medical imaging technologies?
What is transfer learning?
What are the differences between image classification, object detection, and image segmentation?
What is image classification?
What is a Bayesian neural network?
What is a graph neural network?
What is GPT-3?
What is BERT and ALBERT?
What is a transformer?
What is a long-short term memory (LSTM) network?
What is a recurrent neural network?
What is a capsule network, and how does it differ from a convolutional neural network?
What are differences between zero-shot learning, one-shot learning, few-shot learning, and n-shot learning?
What is a Siamese neural network? Draw a schematic diagram of a Siamese neural network.
What is AutoEncoder?
What is U-Net, and what is it best used for?
Use a table to compare the characteristic features of AlexNet, Inception, VGG, ResNet, DenseNet, MobileNet, and EffecientNet.
Compare the features of three commonly used activation functions:REctified Linear Unit (ReLU), hyperbolic tangent, and the sigmoid function.
Explain the terms of convolutional layer, pooling layer, activation layer, dropout layer, and fully connected layer, in the context of convolutional neural networks.
What is a convolutional neural network?
What is an artificial neural network?
What is the difference between machine learning and deep learning?
What are the AutoML, PyCaret, and LazyPredict libraries?
What is ensemble learning?
What is Q-learning?
What is reinforcement learning, and what can it be used for?
What is semi-supervised learning?
What is K-means clustering?
What is unsupervised learning?
How does K-nearest neighbors work?
What is the difference between decision tree and random forest?
What is linear discriminant analysis, and what is principal component analysis?
What is naive Bayes, and how does it work?
What is SVM? What can it be used for?
What is the difference between classification and regression in supervised learning?
What is supervised learning?
What are popular Python AI frameworks?
What are commonly used AI datasets?
What is the standard input and output in a Python program?
What is Caffe2?
What is PyTorch?
What is Keras?
What is TensorFlow?
What is Scikit-Learn?
What is Scipy?
What are the most commonly used datasets in AI? Search the Internet and find five other datasets.
What is a Python virtual environment?
How is Python different from other programming languages such as C/C++ or Java?
What programming languages can be used for AI development? List the advantages and disadvantages of each language.
What types of computer hardware are available for AI development?Do an Internet search and find your ideal laptop/desktop computer for AI development.
Are you optimistic or pessimistic about the future of AI, and why?
Search the Internet and list your own “current state of AI” and give your own predictions for AI.
Search the Internet and list your own top 10 key moments of AI in history.
What are the differences between edge AI and cloud AI?
What are the three types of AI? What is the singularity?
What are AI hypes? Use a diagram to explain the different stages of an AI hype. What are AI winters?
Explain the differences of neural networks, machine learning, and deep learning.
Explain, with a suitable diagram, the three stages of AI development in history.
What is the Turing test, and what is the imitation game?
What is AI? Show a few examples of AI applications in your daily life.
8. Specify several possible codebook vectors for fuzzy expert systems
7. Using the Zurada sensitivity process, calculate the mean square average estimated sensitivity Si,eav for each input of the Iris dataset. What do these sensitivities tell you?
6. Propose an alternative way to display the information contained in a Hinton diagram.
5. Design and run experiments using PSO to plot relationships between the PSO's parameter (or input) values and its performance (or output) values.
4. Assume that you are implementing in the C language the method discussed in this chapter for calculating the Zurada sensitivity two for an m-input, n-output system. Draw the flowchart for your
3. Specify a method for calculating relation factor one for a fuzzy rule-based system.
2. Run the example software to explore the decision hypersurface between each pair of classes. Discuss your results.
1. Run the example software to obtain codebook vectors for each class of iris flower in the Iris dataset. Discuss your results.
7. Use PSO to evolve the weights for different configurations of neural networks that classify the Iris dataset. Use the Mann-Whitney U test to evaluate the configurations.Find two configurations for
6. Use k-fold cross validation to analyze the Iris dataset. Using 3-fold and 10-fold approaches, partition the dataset into 3 and 10 subsets, respectively. What results for training and testing are
5. We are developing a simulation of the game of baseball. We want to measure how well our system simulates the margin of victory. In general, we like the margin of victory to be 1, 2, or 3 runs. A
4. Given the following set of targets and outputs, calculate the average sum-squared error and the normalized error.Repeat your calculations, assuming that thetargets were 0.9 and 0.1 instead of 1
3. Train a neural network on the Iris dataset using the back-propagation implementation(see Chapter 6). Plot an ROC curve for each of the three output PEs, using at least 10 values for the threshold
2. For a pattern set we are using to train a neural network, one-half of the target values are 1 and one-half are 0. Calculate Emean. What happens to Emeanif targets of 0.9 and 0.1 are used instead
1. We have developed a diagnostic system with three possible diagnoses, Q, R, and S, that are equally probable (0.333 each). Assume that the costs associated with the three diagnoses are x, 2x, and
7. How generally applicable is the system diagram of Figure 9.3 to other applications such as analysis of large video data streams? Identify another application area, and draw a diagram analogous to
6. Modify the fuzzy rule system in Listing 9.2, and run the software again to see whether you can obtain better results.
5. Run both the evolutionary fuzzy rule system (m_flaq = 0) and the fuzzy evolutionary fuzzy rule system (m_flaq - 1), and compare the results.
4. If you are asked to use a fuzzy system to adapt the PSO in the implementation of the evolutionary neural network discussed in Chapter 6, what will be the input and output of the fuzzy system?
3. Briefly describe how to use a fuzzy system to adapt the parameters you listed in Exercise 1.
2. Compare the strengths and weaknesses of the four levels of adaptation of genetic algorithms: environment, population, individual, and component.
1. List two parameters that can be adapted to improve a GA's performance at the levels of environment, population, individual, and component.
9. Repeat exercises 5 and 6, but use only 100 patterns to develop the fuzzy rules;then test (classify) all 150 patterns. Describe the differences in the results.

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