There are 3 gradient descent algorithms used for backpropagation in neural networks: **stochastic**, **batch** and **mini-batch.**

- : loss function (calculate error)

- : neural network output (predicted data)

- : real output (train label)

Skip to content# dProgrammer lopez

## Gradient Descent Algorithm

## Stock Market & Deep Learning

## Spacetime Curvature Simulator

## 3D Neural Network Simulator

## Computer Vision – Deep Learning

C++, UE4, AI developer

Mini-batch, Stochastic & Batch Gradient Descent

There are 3 gradient descent algorithms used for backpropagation in neural networks: **stochastic**, **batch** and **mini-batch.**

- : loss function (calculate error)

- : neural network output (predicted data)

- : real output (train label)

C++ Neural Network implementation in stock market

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”

Spacetime real-time simulator in UE4 and C++

In this video, I show you a real-time simulation of space-time distortions that produce gravity because of its mass. The simulator is made in Unreal Engine 4 and C++.

In 1905, Albert Einstein determined that the laws of physics are the same for all non-accelerating observers and that the speed of light in a vacuum was independent of the motion of all observers. This was the theory of special relativity. It introduced a new framework for all of physics and proposed new concepts of space and time.

Einstein then spent 10 years trying to include acceleration in the theory and published his theory of general relativity in 1915. In it, he determined that massive objects cause a distortion in space-time, which is felt as gravity.

Gravitational time dilation formula:

Neural Network implementation in UE4

Our neural network implementation in Unreal Engine 4 can have different purposes, educational or as a tool for data scientist developers.

This tool is really easy to be used: you can change the number of layers and the neurons per layer. In the example showed I have used the MNIST dataset for number recognition, furthermore, I can use different datasets.

If you want to use this tool for educational purposes, you can debug and see how the neural networks work. It is really practical and I recommend whoever wants to start AI. This application is progress, although you would be able to see the advances.

C++ number recognition program using ANN

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.