18CSE483T - INTELLIGENT MACHINING UNIT 1 CLASS NOTES


MAM SAID TO REFER AI NOTES...THIS IS AI CT1 NOTES LINK

AI UNIT 1


THIS IS IM UNIT 1 CLASS NOTES FOR REFERENCE







PLZ DO SEE THE PPT FOR FURTHER REFERENCE

Comments

Post a Comment

Popular posts from this blog

18ECO124T - HUMAN ASSIST DEVICES UNIT 4 & 5 - 12M

Image
12M: SET 1: Write in detail about the pediatric orthosis with a diagram Pediatric orthosis: There are several respects in which a child's orthotic requirements differ from those of an adult and these may extend beyond treatment of the obvious abnormality. Accordingly, before considering specific hardware and research activities, the criteria affecting the performance of an orthosis will be discussed. A child is a particularly dynamic person in terms both of his activity and of his developing personality.  It is important that there is a full appreciation of these factors so that prescription of an orthosis can be made sympathetically and in harmony with his development Human factors: Many physical and emotional factors can affect the success of an orthosis Physical factors can be considered under the headings of deformity, growth and activity Deformity:  Frequently an orthosis is fitted to prevent the development of a deformity, particularly during periods of rapid growth For ...
Read more

18CSE359T - NATURAL LANGUAGE PROCESSING UNIT 1 - 12M

Image
  12M: Using the different NLP methods, identify POS tagging The POS tagging problem is to determine the POS tag for a particular istance of a word POS - Parts of Speech Methods to assign POS to words: Rule based tagging Learning based: Stochastic models: HMM tagging, maximum entropy tagging Rule learning: transformation based tagging Rule based tagging: Two stage architectureENGTWOL tagging Stage 1 - Use dictionary to assign each word a list of potential POS Stage 2 - Hand-written disambiguation rules to fine tune to single POS Disambiguation rule: Adverbial-THAT rule:- Given input: ‘that’ if   (+1 A/ADV/QUANT);  (+2SENT-LIM);  (NOT -1 SVOC/A); then eliminate non-ADV tags else eliminate ADV tag I consider that odd. NOT ADV It isn’t that odd. ADV Stochastic POS Tagging: Pick the most likely tag for the word Example:  She is expected to race tomorrow The race for the cup begins! Transformation based tagging: Combination of rule based tagging and stochast...
Read more

18CSE483T - INTELLIGENT MACHINING UNIT 4 & 5 - 4M

Image
  4M: Compare and contrast different Neural Network Architecture Feedforward NN Feedback NN Self-organizing NN Layers of neurons connected in a forward direction without cycles Layers can form cycles, allowing feedback loops Neurons are organized into a lattice structure Simplicity, ease of implementation Capturing temporal dependencies, dynamic behavior Adaptive learning, ability to detect patterns Suitable for structured data Suitable for temporal or sequential data Suitable for clustering and unsupervised learning Cannot capture temporal dependencies Complexity in training, potential instability Lack of supervised training, interpretability challenges Regression, classification tasks Time-series prediction, dynamic systems Pattern recognition, data visualization Compare and contrast forward propagation and backward propagation Forward propagation Backward propagation Calculate the output of the neural network Adjust weights to minimize the error Input data is passed through the ...
Read more