Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and Neural Networks.
Significant topics covered according to each day:
Day 1 - PyPlot tutorial and basic plotting.
Day 2 - Multilayer Neural Network; Training Multilayer NN.
Day 5 - Bayesian Inference example; NN example
Day 6 - Perceptron Logic Gate example 2
Comprehensive NN Example - Student_Admissions_NN_Example.ipynb-> A brand-new example for solving Classifier problems
-> A comprehensive guide through the making of a single layer NN
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:
Speech recognition
Learning
Planning
Problem solving
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
Knowledge
Reasoning
Problem solving
Perception
Learning
Planning
Ability to manipulate and move objects
Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware. Soft computing is sometimes referred to as computational intelligence.
Soft computing provides an approach to problem-solving using means other than computers. With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models. The tolerance of soft computing allows researchers to approach some problems that traditional computing can't process.
As a field of mathematical and computer study, soft computing has been around since the 1990s. The inspiration was the human mind's ability to form real-world solutions to problems through approximation. Soft computing contrasts with possibility, an approach that is used when there is not enough information available to solve a problem. In contrast, soft computing is used where the problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations.
Soft computing uses component fields of study in:
•Fuzzy logic
•Machine learning •Probabilistic reasoning •Evolutionary computation •Perceptron •Genetic algorithms •Differential algorithms •Support vector machines •Metaheuristics •Swarm intelligence •Ant colony optimization •Particle optimization •Bayesian networks •Artificial neural networks •Expert systems