Coding Club Of Competitive Programmers

  Convolution Neural Network  https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-cnn/ Convolution Operation In mathematics, convolution is a mathematical operation on two functions that produces a third function that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it.  Max-pooling operation, questions based on these operations. Benefits of Convolution operation Convolutions in Neural Networks apply filters to extract features from actual data. A filter could be related to anything, for pictures of humans, one filter could be associated with seeing noses, another with eyes, and so on. Each feature extracted from input data will be in form of activation maps Training process of convolution Neural Network. These are the steps used to training the CNN (Convolutional Neural Network). Steps: Step 1: Upload Dataset. Step 2: The Input layer. Step 3: C...

  Backpropagation algorithm 1. Backpropagation is an algorithm used in the training of feedforward neural networks for supervised learning.  2. Backpropagation efficiently computes the gradient of the loss function with respect to the weights of the network for a single input-output example.  3. This makes it feasible to use gradient methods for training multi-layer networks, updating weights to minimize loss, we use gradient descent or variants such as stochastic gradient descent.  4. The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, iterating backwards one layer at a time from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic programming.  5. The term backpropagation refers only to the algorithm for computing the gradient, but it is often used loosely to refer to the entire learning algorithm. 6. Backpropagation ge...

  Adversarial Search Adversarial search is a search, where we examine the problem which arises when we try to plan ahead of the world and other agents are planning against us. In previous topics, we have studied the search strategies which are only associated with a single agent that aims to find the solution which often expressed in the form of a sequence of actions. But, there might be some situations where more than one agent is searching for the solution in the same search space, and this situation usually occurs in game playing. The environment with more than one agent is termed as multi-agent environment , in which each agent is an opponent of other agent and playing against each other. Each agent needs to consider the action of other agent and effect of that action on their performance. So, Searches in which two or more players with conflicting goals are trying to explore the same search space for the solution, are called adversarial searches, often known as Gam...

  Information extraction is the process of extracting specific (pre-specified) information from textual sources. One of the most trivial examples is when your email extracts only the data from the message for you to add in your Calendar. Gathering detailed structured data from texts, information extraction enables: The automation of tasks such as smart content classification, integrated search, management and delivery; Data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc. How Does Information Extraction Work? typically, for structured information to be extracted from unstructured texts, the following main subtasks are involved: Pre-processing of the text – this is where the text is prepared for processing with the help of computational linguistics tools such as tokenization, sentence splitting, morphological analysis, etc. Finding and classifying concepts – this is where mentions of people, things, locations, events and other pre-sp...

  Rationality: The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of following points: Performance measure which defines the success criterion. Agent prior knowledge of its environment. Best possible actions that an agent can perform. The sequence of percepts.

  Simulated Annealing: A hill-climbing algorithm which never makes a move towards a lower value guaranteed to be incomplete because it can get stuck on a local maximum. And if algorithm applies a random walk, by moving a successor, then it may complete but not efficient.   Simulated Annealing   is an algorithm which yields both efficiency and completeness. In mechanical term   Annealing   is a process of hardening a metal or glass to a high temperature then cooling gradually, so this allows the metal to reach a low-energy crystalline state. The same process is used in simulated annealing in which the algorithm picks a random move, instead of picking the best move. If the random move improves the state, then it follows the same path. Otherwise, the algorithm follows the path which has a probability of less than 1 or it moves downhill and chooses another path.

 The Kakinada Experiment 1.Assuming need for achievement plays a vital role in promoting economic growth 2.Mcclelland has tried to induce achievement motivation in adult and provide them with an urge to improve their lot because unindicted achievement motivation results in long waiting before it bears fruit.  3.Such an inducement may help break the barrier of“limited aspirations”.  4.For this purpose, he conducted experiments with groups of businessmen in America, Mexico and Bombay. Later he carried out a full-fledged programme in the Kakinada city of Andhra Pradesh. Kakinada is a well-developed distinct town of a population of around one lakh with high literacy and a modest industrial structure. Objective of the program: 1.to break the barrier of “limited aspirations” by inducing achievement motivation.  2..The project which began in January 1964 consisted of recruiting batches of personnel drawn from business and industrial community of this town and putting them...