Computer vision has recently experienced a boom in the AI field. It has many applications and it could be very useful to provide a machine the sense. It is used in words and number recognition, to classify objects in clusters and it also gives us the possibility to make autonomous cars. We have seen posts about autonomous cars and computer vision is the next step to achieve this.
We have used a simple backpropagation neural network to recognize the numbers that implement mini-batch learning, momentum, Xavier initialization, and normalization. It is really an advanced ANN that classifies numbers in 10 cluster that represents each one a number between 0 and 9.
Evolutionary algorithm learning process of cars in UE4 C++
AI is a new field that appeared a few decades ago and has seen an 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.
Gravity simulator that imitates Solar System planets movement in 3d
This project enables us to simulate gravitational forces and apply them to objects with a certain mass. It calculates the forces of n-body objects and their interactions, it can be used to see simulate the Solar System conditions. The gravity simulator could have educational porpuses.
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.