I wrote one of the most comprehensive deep learning tutorials for using PyTorch for Numer.AI stock market prediction.
It is a binary classification problem, and the tutorial includes Kaggle style ROC_AUC plots which are rarely seen in PyTorch.
Comments are welcomed, I am sure I have bugs and mistakes.

  
      github.com
  
  
    QuantScientist/Deep-Learning-Boot-Camp/blob/master/day02-PyTORCH-and-PyCUDA/PyTorch/18-PyTorch-NUMER.AI-Binary-Classification-BCELoss.ipynb 498
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Deep Learning Bootcamp November 2017, GPU Computing for Data Scientists\n",
    "\n",
    "<img src=\"../images/bcamp.png\" align=\"center\">\n",
    "\n",
    "## 18  PyTorch NUMER.AI  Deep Learning Binary Classification using BCELoss \n",
    "\n",
    "Web: https://www.meetup.com/Tel-Aviv-Deep-Learning-Bootcamp/events/241762893/\n",
    "\n",
    "Notebooks: <a href=\"https://github.com/QuantScientist/Data-Science-PyCUDA-GPU\"> On GitHub</a>\n",
    "\n",


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2017-09-04 10_24_49-Deep-Learning-Boot-Camp_18-PyTorch-NUMER.AI-Binary-Classification-BCELoss.ipynb .png888×632 31.9 KB

Best,