What is simulated annealing Algorithm?
The process is of heating and cooling a metal to change its internal structure. Although, for modifying its physical properties is known as annealing. As soon as the metal cools, it forms a new structure. Also, metal is going to retain its newly obtained properties. Although, we have to keep the variable temperature in a simulated annealing process.
First, we have to set high temperature. Then, left it to allow “cool” slowly with the proceeding algorithm. Further, if there is high temperature, algorithm accepts worse solutions with high frequency.
Start
Initialize k = 0; L = integer number of variables;
From i → j, search the performance difference Δ.
If Δ random(0,1) then accept;
Repeat steps 1 and 2 for L(k) steps.
k = k + 1;
Repeat steps 1 through 4 till the criteria matches.
What are expert Systems Limitations?
Basically, we have noticed that no technology can offer an easy and complete solution. Also, large systems are too costly. Although, they require significant development time and computer resources.
Also, ESs have their limitations which include −
- Limitations of the technology
- Difficult knowledge acquisition
- ES are difficult to maintain
- High development cost
What is Breadth-First Search Algorithm?
Basically, we have to start searching for the root node. And continue through neighboring nodes first. Further, moves towards next level of nodes. Moreover, till the solution is found, generates one tree at a time. As this search can be implemented using FIFO queue data structure. This method provides the shortest path to the solution. FIFO(First in First Out). If the branching factor (average number of child nodes for a given node) = b and depth = d, the number of nodes at level d = bd. The total no of nodes created in worst case is b + b2 + b3 + … + bd.
What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?
DL is a subset of ML, which is the subset of AI. Hence, AI is the all-encompassing concept that initially erupted in computer science. It was then followed by ML that thrived later, and lastly DL, that is now promising to escalate the advances of AI to another level.
What is overfitting? How can it be overcome in Machine Learning?
When the machine learning algorithm tries to capture all the data points, and hence, as a result, captures noise also, then overfitting occurs in the model. Due to this overfitting issue, the algorithm shows the low bias, but the high variance in the output. Overfitting is one of the main issues in machine learning.
Methods to avoid Overfitting in ML:
- Cross-Validation
- Training With more data
- Regularization
- Ensembling
- Removing Unnecessary Features
- Early Stopping the training.
Mention some popular Machine Learning Algorithms?
Some of the popular Machine Learning algorithms are:
- Logistic regression
- Linear regression
- Decision trees
- Support vector machines
What is the importance of N.L.P?
We can understand the advantage of natural language programming in an easy way as we consider two statements:
“Cloud computing insurance should be part of every service level agreement”
“A good S.L.A ensures an easier night’s sleep — even in the cloud.”
Generally, if an individual is used to of NLP, in an entity, a person will recognize cloud computing program. Also, a cloud is an abbreviated form of cloud computing.
Basically, in human language, these type of vague elements appears frequently. Although, machine learning algorithms are historically bad at interpreting. Moreover, many improvements take place in deep learning and artificial intelligence. And interpret them effectively.
Explain a bidirectional search algorithm. What is it?
A bidirectional search algorithm runs two simultaneous searches. The first go forward from the initial state, and the second goes backward from the goal state. They both meet at a common point, and that’s when the search ends—the goal state links with the initial state in a reverse manner.
What is a Bayesian network, and why is it important in AI?
Bayesian networks are the graphical models that are used to show the probabilistic relationship between a set of variables. It is a directed cycle graph that contains multiple edges, and each edge represents a conditional dependency.
Bayesian networks are probabilistic, because these networks are built from a probability distribution, and also use probability theory for prediction and anomaly detection. It is important in AI as it is based on Bayes theorem and can be used to answer the probabilistic questions.
In speech recognition which model gives the probability of each word following each word?
Biagram model gives the probability of each word following each other word in speech recognition.