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What are some of the algorithms used for hyperparameter optimization? There are many algorithms that are used for hyperparameter optimization, and following are the three main ones that are widely used: Bayesian optimization Grid search Random search Mention the difference between breadth first search and best first search in artificial intelligence? These are the two … Read more
Can you list some disadvantages related to linear models? There are many disadvantages to using linear models, but the main ones are: Errors in linearity assumptions Lacks autocorrelation It can’t solve overfitting problems You can’t use it to calculate outcomes or binary outcomes In Inductive Logic Programming what needed to be satisfied? The objective of … Read more
List the steps involved in Machine Learning. Data collection Data preparation Choosing an appropriate model Training the dataset Evaluation Parameter tuning Predictions What steps would you take to evaluate the effectiveness of your ML model? You have to first split the data set into training and test sets. You also have the option of using … Read more
What’s the difference between inductive, deductive, and abductive learning? Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning. There are three literals in (top-down) inductive learning: Arithmetic literals Equality and inequality Predicates In deductive … Read more
What is a depth-first search algorithm? Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement. How … Read more
What is TensorFlow? TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs. Mention the difference between statistical AI and Classical AI ? Statistical AI is more concerned … Read more
Give an explanation on the difference between strong AI and weak AI? Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools. … Read more
How would you go about choosing an algorithm to solve a business problem? First, you have to develop a “problem statement” that’s based on the problem provided by the business. This step is essential because it’ll help ensure that you fully understand the type of problem and the input and the output of the problem … Read more
What are the different algorithm techniques you can use in AI and ML? Some algorithm techniques that can be leveraged are: Learning to learn Reinforcement learning (deep adversarial networks, q-learning, and temporal difference) Semi-supervised learning Supervised learning (decision trees, linear regression, naive bayes, nearest neighbor, neural networks, and support vector machines) Transduction Unsupervised learning (association … Read more
What is an expert system? What are the characteristics of an expert system? An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include: High performance Adequate response … Read more