phizaz

class MyLayer(Layer):
    def __init__(self, **kwargs):
        ...
        super(MyConv, self).__init__(**kwargs)

    def build(self, input_shape):
        ...
        super(MyConv, self).build(input_shape)

    def call(self, x):

phizaz

phizaz

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)

phizaz

# To the extent possible under law, the author(s) have dedicated all
# copyright and related and neighboring rights to this software to the
# public domain worldwide. This software is distributed without any warranty.
# You should have received a copy of the CC0 Public Domain Dedication along
# with this software.
# If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.

# ~/.bash_profile: executed by bash(1) for login shells.

# The copy in your home directory (~/.bash_profile) is yours, please

phizaz

# get needed for installing VIM binding
RUN apt-get update
RUN apt-get install -y git

# install Jupyter extensions (https://github.com/ipython-contrib/jupyter_contrib_nbextensions)
RUN pip install jupyter_contrib_nbextensions
RUN jupyter contrib nbextension install --user

# install VIM binding for Jupyter (https://github.com/lambdalisue/jupyter-vim-binding)
RUN 	mkdir -p $(jupyter --data-dir)/nbextensions \

phizaz

using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;

namespace ProducerConsumer
{

phizaz

using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;

namespace ProducerConsumer
{

phizaz

using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;

namespace ProducerConsumer
{

phizaz

Q-Learning for algorithm trading

Q-Learning background

by Konpat

Q-Learninng is a reinforcement learning algorithm, Q-Learning does not require the model and the full understanding of the nature of its environment, in which it will learn by trail and errors, after which it will be better over time. And thus proved to be asymtotically optimal.

• you need first to understand the Markov Decision Process, which is a graph consisting of (states, actions, rewards) denoting {S}, {A}, {R}
• State (S)

phizaz

// Place your key bindings in this file to overwrite the defaults
[{
        "key": "ctrl+a",
        "command": "editor.action.selectAll"
    },
    {
        "key": "ctrl+x",
        "command": "editor.action.clipboardCutAction"
    },
    {