The important considerations when choosing machine learning algorithms: Type of problem: It is obvious that algorithms have been designd to solve specific problems. So, it is important to know what type of problem we are dealing with and what kind of algorithm works best for each type of probRead more
The important considerations when choosing machine learning algorithms:
Type of problem: It is obvious that algorithms have been designd to solve specific problems. So, it is important to know what type of problem we are dealing with and what kind of algorithm works best for each type of problem. I don’t want to go into much detail but at high level, machine learning algorithms can be classified into Supervised, Unsupervised and Reinforcement learning. Supervised learning by itself can be categorized into Regression, Classification, and Anomoly Detection.
Size of training set: This factor is a big player in our choice of algorithm. For a small training set, high bias/low variance classifiers (e.g., Naive Bayes) have an advantage over low bias/high variance classifiers (e.g., kNN), since the latter will overfit. But low bias/high variance classifiers start to win out as training set grows (they have lower asymptotic error), since high bias classifiers aren’t powerful enough to provide accurate models [1].
Accuracy: Depending on the application, the required accuracy will be different. Sometimes an approximation is adequate, which may lead to huge reduction in processing time. In addition, approximate methods are very robust to overfitting.
Training time: Various algorithms have different running time. Training time is normally function of size of dataset and the target accuracy.
Linearity: Lots of machine learning algorithms such as linear regression, logistic regression, and support vector machines make use of linearity. These assumptions aren’t bad for some problems, but on others they bring accuracy down. Despite their dangers, linear algorithms are very popular as a first line of attack. They tend to be algorithmically simple and fast to train.
Number of parameters: Parameters affect the algorithm’s behavior, such as error tolerance or number of iterations. Typically, algorithms with large numbers parameters require the most trial and error to find a good combination. Even though having many parameters typically provides greater flexibility, training time and accuracy of the algorithm can sometimes be quite sensitive to getting just the right settings.
Number of features: The number of features in some datasets can be very large compared to the number of data points. This is often the case with genetics or textual data. The large number of features can bog down some learning algorithms, making training time unfeasibly long. Some algorithms such as Support Vector Machines are particularly well suited to this case [2,3].
Below is an algorithm cheatsheet provided by scikit-learn (works as rule of thumb), which I believe it has implicitely considered all the above factors in making recommendation for choosing the right algorithm. But it doesn’t work for all situations and we need to have a deeper understanding of these algorithms to employ the best one for a unique problem.
Software quality assurance (SQA) is a process which assures that all software engineering processes, methods, activities and work items are monitored and comply against the defined standards. These defined standards could be one or a combination of any like ISO 9000, CMMI model, ISO15504, etc.Read more
Software quality assurance (SQA) is a process which assures that all software engineering processes, methods, activities and work items are monitored and comply against the defined standards. These defined standards could be one or a combination of any like ISO 9000, CMMI model, ISO15504, etc.
There are several techniques/tools for SQA. Auditing is the chief technique that is widely adopted. However, we have a few other significant techniques as well.
Various SQA Techniques include:
Auditing: Auditing involves inspection of the work products and its related information to determine if the set of standard processes were followed or not.
Reviewing: A meeting in which the software product is examined by both the internal and external stakeholders to seek their comments and approval.
Code Inspection: It is the most formal kind of review that does static testing to find bugs and avoid defect growth in the later stages. It is done by a trained mediator/peer and is based on rules, checklist, entry and exit criteria. The reviewer should not be the author of the code.
Design Inspection: Design inspection is done using a checklist that inspects the below areas of software design:
General requirements and design
Functional and Interface specifications
Conventions
Requirement traceability
Structures and interfaces
Logic
Performance
Error handling and recovery
Testability, extensibility
Coupling and cohesion
Simulation: Simulation is a tool that models the real-life situation in order to virtually examine the behavior of the system under study.
Functional Testing: It is a QA technique which verifies what the system does without considering how it does. This type of black box testing mainly focuses on testing the system specifications or features.
Standardization: Standardization plays a crucial role in quality assurance. It decreases the ambiguity and guesswork, thus ensuring quality.
Static Analysis: It is a software analysis that is done by an automated tool without actually executing the program. This technique is highly used for quality assurance in medical, nuclear and aviation software. Software metrics and reverse engineering are some popular forms of static analysis.
Walkthroughs: Software walkthrough or code walkthrough is a kind of peer review where the developer guides the members of the development team to go through the product and raise queries, suggest alternatives, make comments regarding possible errors, standard violations or any other issues.
Path Testing: It is a white box testing technique where the complete branch coverage is ensured by executing each independent path at least once.
Stress Testing: This type of testing is done to check how robust a system is by testing it under heavy load i.e. beyond normal conditions.
Six Sigma: Six Sigma is a quality assurance approach that aims at nearly perfect products or services. It is widely applied in many fields including software. The main objective of six sigma is process improvement so that the produced software is 99.76 % defect free.
(i) TEST: TEST pin is examined by the “WAIT” instruction. If the TEST pin is Low, execution continues. Otherwise the processor waits in an “idle” state. This input is synchronized internally during each clock cycle on the leading edge of CLK. (ii) RQ/GT0 and RQ/GT1 : Request/Grant. These pinsRead more
(i) TEST:
TEST pin is examined by the “WAIT” instruction. If the TEST pin is Low, execution continues. Otherwise the processor waits in an “idle” state. This input is synchronized internally during each clock cycle on the leading edge of CLK.
(ii) RQ/GT0 and RQ/GT1 :
Request/Grant. These pins are used by local bus masters used to force the microprocessor to release the local bus at the end of the microprocessor’s current bus cycle. Each of the pin is bi-directional. RQ’/GT0′ have higher priority than RQ’/GT1′.
(iii) QS0 and QS1 :
Queue Status. These signals indicate the status of the internal 8086 instruction queue according to the table shown below
QS1
QS0
Status
0
0
No operation
0
1
First byte of op code from queue
1
0
Empty the queue
1
1
Subsequent byte from queue
(iv) s0, s1, s2. :
Status pins. These pins are active during T4, T1 and T2 states and is returned to passive state (1,1,1 during T3 or Tw (when ready is inactive). These are used by the 8288 bus controller for generating all the memory and I/O operation) access control signals. Any change in S2, S1, S0 during T4 indicates the beginning of a bus cycle.
The flag register in 8085 microprocessor contains 5 flags that is, Sign Flag, ZeroFlag, Auxiliary Carry Flag, Parity Flag and Carry Flag. while, The flag register in 8086 microprocessor contains9flags that is, Overflow Flag,Direction Flag, Interrupt Flag, Trap Flag, Sign Flag, Zero Flag, Auxiliary FRead more
The flag register in 8085 microprocessor contains 5 flags that is, Sign Flag, Zero Flag, Auxiliary Carry Flag, Parity Flag and Carry Flag.
while,
The flag register in 8086 microprocessor contains9flags that is, Overflow Flag, Direction Flag, Interrupt Flag, Trap Flag, Sign Flag, Zero Flag, Auxiliary Flag, Carry Flag, and Parity Flag.
8086 Flags:
Overflow Flag (O) – This flag will be set (1) if the result of a signed operation is too large to fit in the number of bits available to represent it, otherwise reset (0). After any operation, if D[6] generates any carry and passes to D[7] OR if D[6] does not generates carry but D[7] generates, overflow flag becomes set, i.e., 1. If D[6] and D[7] both generate carry or both do not generate any carry, then overflow flag becomes reset, i.e., 0.
Directional Flag (D) – This flag is specifically used in string instructions. If directional flag is set (1), then access the string data from higher memory location towards lower memory location. If directional flag is reset (0), then access the string data from lower memory location towards higher memory location.
Interrupt Flag (I) – This flag is for interrupts. If interrupt flag is set (1), the microprocessor will recognize interrupt requests from the peripherals. If interrupt flag is reset (0), the microprocessor will not recognize any interrupt requests and will ignore them.
Trap Flag (T) – This flag is used for on-chip debugging. Setting trap flag puts the microprocessor into single step mode for debugging. In single stepping, the microprocessor executes a instruction and enters into single step ISR. If trap flag is set (1), the CPU automatically generates an internal interrupt after each instruction, allowing a program to be inspected as it executes instruction by instruction. If trap flag is reset (0), no function is performed.
How do you make sure which Machine Learning Algorithm to …
The important considerations when choosing machine learning algorithms: Type of problem: It is obvious that algorithms have been designd to solve specific problems. So, it is important to know what type of problem we are dealing with and what kind of algorithm works best for each type of probRead more
The important considerations when choosing machine learning algorithms:
Type of problem: It is obvious that algorithms have been designd to solve specific problems. So, it is important to know what type of problem we are dealing with and what kind of algorithm works best for each type of problem. I don’t want to go into much detail but at high level, machine learning algorithms can be classified into Supervised, Unsupervised and Reinforcement learning. Supervised learning by itself can be categorized into Regression, Classification, and Anomoly Detection.
Size of training set: This factor is a big player in our choice of algorithm. For a small training set, high bias/low variance classifiers (e.g., Naive Bayes) have an advantage over low bias/high variance classifiers (e.g., kNN), since the latter will overfit. But low bias/high variance classifiers start to win out as training set grows (they have lower asymptotic error), since high bias classifiers aren’t powerful enough to provide accurate models [1].
Accuracy: Depending on the application, the required accuracy will be different. Sometimes an approximation is adequate, which may lead to huge reduction in processing time. In addition, approximate methods are very robust to overfitting.
Training time: Various algorithms have different running time. Training time is normally function of size of dataset and the target accuracy.
Linearity: Lots of machine learning algorithms such as linear regression, logistic regression, and support vector machines make use of linearity. These assumptions aren’t bad for some problems, but on others they bring accuracy down. Despite their dangers, linear algorithms are very popular as a first line of attack. They tend to be algorithmically simple and fast to train.
Number of parameters: Parameters affect the algorithm’s behavior, such as error tolerance or number of iterations. Typically, algorithms with large numbers parameters require the most trial and error to find a good combination. Even though having many parameters typically provides greater flexibility, training time and accuracy of the algorithm can sometimes be quite sensitive to getting just the right settings.
Number of features: The number of features in some datasets can be very large compared to the number of data points. This is often the case with genetics or textual data. The large number of features can bog down some learning algorithms, making training time unfeasibly long. Some algorithms such as Support Vector Machines are particularly well suited to this case [2,3].
Below is an algorithm cheatsheet provided by scikit-learn (works as rule of thumb), which I believe it has implicitely considered all the above factors in making recommendation for choosing the right algorithm. But it doesn’t work for all situations and we need to have a deeper understanding of these algorithms to employ the best one for a unique problem.
Note: For diagram download the below attachment
See lessAn Online bookstore is to be implemented. This project is …
Answer for design document of online book store: Download below attachment:
Answer for design document of online book store:
Download below attachment:
See lessAnalyze the role of testing tools in maintaining the quality …
Follow the below link for the answer: https://sikshapath.in/question/analyze-the-role-of-testing-tools-in-maintaining-the-quality/
Follow the below link for the answer:
See lessSoftware quality assurance is a critical mechanism to analyze the …
Software quality assurance (SQA) is a process which assures that all software engineering processes, methods, activities and work items are monitored and comply against the defined standards. These defined standards could be one or a combination of any like ISO 9000, CMMI model, ISO15504, etc.Read more
Software quality assurance (SQA) is a process which assures that all software engineering processes, methods, activities and work items are monitored and comply against the defined standards. These defined standards could be one or a combination of any like ISO 9000, CMMI model, ISO15504, etc.
There are several techniques/tools for SQA. Auditing is the chief technique that is widely adopted. However, we have a few other significant techniques as well.
Various SQA Techniques include:
- Auditing: Auditing involves inspection of the work products and its related information to determine if the set of standard processes were followed or not.
- Reviewing: A meeting in which the software product is examined by both the internal and external stakeholders to seek their comments and approval.
- Code Inspection: It is the most formal kind of review that does static testing to find bugs and avoid defect growth in the later stages. It is done by a trained mediator/peer and is based on rules, checklist, entry and exit criteria. The reviewer should not be the author of the code.
- Design Inspection: Design inspection is done using a checklist that inspects the below areas of software design:
- General requirements and design
- Functional and Interface specifications
- Conventions
- Requirement traceability
- Structures and interfaces
- Logic
- Performance
- Error handling and recovery
- Testability, extensibility
- Coupling and cohesion
- Simulation: Simulation is a tool that models the real-life situation in order to virtually examine the behavior of the system under study.
- Functional Testing: It is a QA technique which verifies what the system does without considering how it does. This type of black box testing mainly focuses on testing the system specifications or features.
- Standardization: Standardization plays a crucial role in quality assurance. It decreases the ambiguity and guesswork, thus ensuring quality.
- Static Analysis: It is a software analysis that is done by an automated tool without actually executing the program. This technique is highly used for quality assurance in medical, nuclear and aviation software. Software metrics and reverse engineering are some popular forms of static analysis.
- Walkthroughs: Software walkthrough or code walkthrough is a kind of peer review where the developer guides the members of the development team to go through the product and raise queries, suggest alternatives, make comments regarding possible errors, standard violations or any other issues.
- Path Testing: It is a white box testing technique where the complete branch coverage is ensured by executing each independent path at least once.
- Stress Testing: This type of testing is done to check how robust a system is by testing it under heavy load i.e. beyond normal conditions.
- Six Sigma: Six Sigma is a quality assurance approach that aims at nearly perfect products or services. It is widely applied in many fields including software. The main objective of six sigma is process improvement so that the produced software is 99.76 % defect free.
See lessDescribe the function of the following pins in 8086 maximum mode of operation
(i) TEST: TEST pin is examined by the “WAIT” instruction. If the TEST pin is Low, execution continues. Otherwise the processor waits in an “idle” state. This input is synchronized internally during each clock cycle on the leading edge of CLK. (ii) RQ/GT0 and RQ/GT1 : Request/Grant. These pinsRead more
(i) TEST:
(ii) RQ/GT0 and RQ/GT1 :
(iii) QS0 and QS1 :
(iv) s0, s1, s2. :
What are the main difference between flag register of 8086 and 8085?
The flag register in 8085 microprocessor contains 5 flags that is, Sign Flag, ZeroFlag, Auxiliary Carry Flag, Parity Flag and Carry Flag. while, The flag register in 8086 microprocessor contains9flags that is, Overflow Flag,Direction Flag, Interrupt Flag, Trap Flag, Sign Flag, Zero Flag, Auxiliary FRead more
The flag register in 8085 microprocessor contains 5 flags that is, Sign Flag, Zero
Flag, Auxiliary Carry Flag, Parity Flag and Carry Flag.
while,
The flag register in 8086 microprocessor contains9flags that is, Overflow Flag,
Direction Flag, Interrupt Flag, Trap Flag, Sign Flag, Zero Flag, Auxiliary Flag, Carry Flag, and Parity Flag.
8086 Flags:
Overflow Flag (O) – This flag will be set (1) if the result of a signed operation is too large to fit in the number of bits available to represent it, otherwise reset (0). After any operation, if D[6] generates any carry and passes to D[7] OR if D[6] does not generates carry but D[7] generates, overflow flag becomes set, i.e., 1. If D[6] and D[7] both generate carry or both do not generate any carry, then overflow flag becomes reset, i.e., 0.
Directional Flag (D) – This flag is specifically used in string instructions.
If directional flag is set (1), then access the string data from higher memory location towards lower memory location.
If directional flag is reset (0), then access the string data from lower memory location towards higher memory location.
Interrupt Flag (I) – This flag is for interrupts.
If interrupt flag is set (1), the microprocessor will recognize interrupt requests from the peripherals.
If interrupt flag is reset (0), the microprocessor will not recognize any interrupt requests and will ignore them.
Trap Flag (T) – This flag is used for on-chip debugging. Setting trap flag puts the microprocessor into single step mode for debugging. In single stepping, the microprocessor executes a instruction and enters into single step ISR.
See lessIf trap flag is set (1), the CPU automatically generates an internal interrupt after each instruction, allowing a program to be inspected as it executes instruction by instruction.
If trap flag is reset (0), no function is performed.