AI is a new ﬁeld that appeared a few decades ago and has seen unprecedented growth. The AI approach has been used in numerous ﬁelds, such as ﬁnance, medicine, music, customer service, and transportation. Some of the big challenges we have to cope with are the computing power and lack of documentation. Big companies such as Google and Apple do not share advances in this ﬁeld, the secretism characterizes this scientiﬁc ﬁeld and thus the information we can get is very restrictive. In this work, we introduce a model of an autonomous car and then examine diﬀerent algorithms capable of driving the car in a simulated. We also discuss the diﬃculties we need to deal with such as local minimums, losing diversity, ﬁtness functions, localization, performance, and future applications.
A space elevator is a proposed type of planet-to-space transportation system. The main component would be a cable (also called a tether) anchored to the surface and extending into space. The design would permit vehicles to travel along the cable from a planetary surface, such as the Earth’s, directly into space or orbit, without the use of large rockets. An Earth-based space elevator would consist of a cable with one end attached to the surface near the equator and the other end in space beyond geostationary orbit (35,786 km altitude). The competing forces of gravity, which is stronger at the lower end, and the outward/upward centrifugal force, which is stronger at the upper end, would result in the cable being held up, under tension, and stationary over a single position on Earth. With the tether deployed, climbers could repeatedly climb the tether to space by mechanical means, releasing their cargo to orbit. Climbers could also descend the tether to return cargo to the surface from orbit.
Genetic algorithm implementation in neural network
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity.
It is an algorithm that was inspired by the theory of evolution by Charles Darwin. It simulates the process of natural selection where the fittest individuals have higher probabilities to transfer their genes to the next generation. It is usually divided into 5 parts:
C++ Neural Network Backpropagation tutorial from scratch.
In this playlist, I teach the neural network architecture and the learning processes to make the ANN able to learn from a dataset.
The Tutorials are divided in each part of the neural network and we start coding it in C++ in Visual Studio 2017. Once you have completed the tutorial you will be able to design your own neural network and optimize it.