Genetic algorithm and Backpropagation in autonomous cars
Autonomous cars need learning in order to recognize their environment and behave consistently. Neural networks give us the opportunity to develop complex behaviors and tasks like driving.
I have implemented two algorithms that are widely used in machine learning: backpropagation (supervised learning) and genetic algorithm (reinforcement learning).
I have used C++ in this project, the program attempts to predict updated stock market data. I have used a neural network library created by myself to train our model an used it to predict Stock market data.
The data is obtained from IEX trading that is an API for investors and it is updated via HTTP requests. The web-scraping program is built from scratch. Once we have trained the neural net, it reaches a high accuracy, 96%. Continue reading “Stock Market & Deep Learning”
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.