Category: API

CPU vs GPU | Neural Network

GPU vs CPU neural network

I have done two libraries for AI,

  • CPU with Eigen 1 2
  • GPU with CUDA 1 2

They are optimized and work properly. However, there are significant differences in performance.

The GPU library can adapt to other Nvidia graphic card and furthermore improve the time that I obtain within my circumstances. In addition, the graphics card industry is constantly improving their models and we have recently seen the clusters of the graphics card in the business.

The CPU library is stable and can work on any computer because it does not rely on Nvidia hardware, it is a completely cross-platform library that only needs Eigen library which is compatible with every OS.

I have used for the test: i7-7700k vs Nvidia GeForce GTX 1070

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AI + GPU

Deep Neural Network in GPU

I have been developing a neural network API for GPUs. It is based on CUDA technology and I tried to exploit the full potential of the computer.

Most of us, do not know the real potential of GPUs in AI. This kind of hardware is specialized in logical operations and it was used by the industry of engines and videogames. Some years ago, we have experienced a ‘boom’ in graphics cards, the improvement in speed, price and the ease of programming had triggered a new mother-lode.

GPUs provide a bunch of parallelism algorithms to improve performance.

As we know, neural networks use matrix-to-matrix and matrix-to-vector operations. We can take advance in the optimization of them. Unfortunately, feed-forward and backpropagation are linear functions, they can neither be optimized nor parallelized.

    \[Output=\sigma(W*\sigma(...\sigma(W*Input+Bias)...)+Bias)\]

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Stock Market & Deep Learning

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”

Stock Market Data

Web-Scraping Stock Market data using C++ API

This is a complex C++ program that is used as an interface to the client, we only need to execute the main thread and it will automatically update stock market data and store it using the JSON format.

We have used IEX API which is free and has more than 10 indices. It obtains information in real time and it is updated whenever you execute it.

Link

The code it for free on a GitHub repository: StockMarketData Repository

Stock market data & deep learning

Football Web Scraping

Football web scraping in Java

This application collects data from some website and transforms it into CSV files. The program is executed in parallel and is high-performance.

  • Historical data

It can store statistics, classification, schedule and team information from 1920 to this year.

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