shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

lawloretienne

Table Of Contents

• Data Structures
  • Linked Lists
  • Trees
    • Binary Trees
    • Binary Search Tree
    • Red-Black Tree
    • AVL Tree
• Tries

slode

"""
MIT License

Copyright (c) 2017 Stian Lode,
                   [email protected]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

druska

/*****************************************************************************
 *                QuantCup 1:   Price-Time Matching Engine
 *
 * Submitted by: voyager
 *
 * Design Overview:
 *   In this implementation, the limit order book is represented using
 *   a flat linear array (pricePoints), indexed by the numeric price value.
 *   Each entry in this array corresponds to a specific price point and holds
 *   an instance of struct pricePoint. This data structure maintains a list