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Weave (Web-based Analysis and Visualization Environment) Design and publish beautiful maps. Data Sets. NNDesign. Open-source, distributed deep learning for the JVM. Winter1516 lecture12. Tensorflow/tensorflow/examples/label_image at master · tensorflow/tensorflow. Winter1516 lecture12. Data Sets. Statistics - Prediction using Recurrent Neural Network on Time series dataset.

Technical Analysis: Chart Patterns. By Cory Janssen, Chad Langager and Casey Murphy A chart pattern is a distinct formation on a stock chart that creates a trading signal, or a sign of future price movements.

Technical Analysis: Chart Patterns

Chartists use these patterns to identify current trends and trend reversals and to trigger buy and sell signals. In the first section of this tutorial, we talked about the three assumptions of technical analysis, the third of which was that in technical analysis, history repeats itself. The theory behind chart patters is based on this assumption. The idea is that certain patterns are seen many times, and that these patterns signal a certain high probability move in a stock.

While there are general ideas and components to every chart pattern, there is no chart pattern that will tell you with 100% certainty where a security is headed. Head and Shoulders This is one of the most popular and reliable chart patterns in technical analysis. Python-neural-network/README.md at master · jorgenkg/python-neural-network.

Implementing a Neural Network from Scratch in Python – An Introduction – WildML. Get the code: To follow along, all the code is also available as an iPython notebook on Github.

Implementing a Neural Network from Scratch in Python – An Introduction – WildML

In this post we will implement a simple 3-layer neural network from scratch. Welcome — Theano 0.8.2 documentation. How to Seek Help¶ The appropriate venue for seeking help depends on the kind of question you have.

Welcome — Theano 0.8.2 documentation

Speeding up your Neural Network with Theano and the GPU – WildML. Get the code: The full code is available as an Jupyter/iPython Notebook on Github!

Speeding up your Neural Network with Theano and the GPU – WildML

In a previous blog post we build a simple Neural Network from scratch. Let’s build on top of this and speed up our code using the Theano library. With Theano we can make our code not only faster, but also more concise! What is Theano? Theano describes itself as a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays. Because Neural Networks are easily expressed as graphs of computations, Theano is a great fit. The Setup. Implementing a Neural Network from Scratch in Python – An Introduction – WildML.

Deepdream/dream.ipynb at master · google/deepdream. Architecture - When to Redis? When to MongoDB? Redis as the primary data store? WTF?! The web is abound with warnings and cautionary tales about going this route.

Redis as the primary data store? WTF?!

There are horror stories about lost data, hitting memory limits, or people unable to effectively manage the data within Redis, so you might be wondering “What on earth were you thinking?! “ So here is our story, why we decided to use Redis anyway, and how we overcame those issues. First of all, I want to stress that most applications shouldn’t even worry about the engineering hurdles involved with going this route. It was important for our use case, but we may very well be an edge case. Redis is Fast. Like memcached, everything is held in memory. Observable. In ReactiveX an observer subscribes to an Observable .

Observable

The introduction to Reactive Programming you've been missing. The introduction to Reactive Programming you've been missing (by @andrestaltz) This tutorial as a series of videos If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

The introduction to Reactive Programming you've been missing

So you're curious in learning this new thing called Reactive Programming, particularly its variant comprising of Rx, Bacon.js, RAC, and others. André Staltz. Demystifying Deep Convolutional Neural Networks - Adam Harley (2014) Adam Harley (adam.harley<at>ryerson.ca) Version 1.1 Abstract.

Demystifying Deep Convolutional Neural Networks - Adam Harley (2014)

This document explores the mathematics of deep convolutional neural networks. We begin at the level of an individual neuron, and from there examine parameter tuning, fully-connected networks, error minimization, back-propagation, convolutional networks, and finally deep networks. The report concludes with experiments on geometric invariance, and data augmentation. Contents. Neural networks and deep learning. In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks.

Neural networks and deep learning

That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow nets. But while the news from the last chapter is discouraging, we won't let it stop us. In this chapter, we'll develop techniques which can be used to train deep networks, and apply them in practice. We'll also look at the broader picture, briefly reviewing recent progress on using deep nets for image recognition, speech recognition, and other applications.

List of 14+ Image Recognition APIs - Mashape Blog. 50 Top Open Source Tools for Big Data. Turn your $60 router into a $600 router. The complete guide to multiple monitors. While you may already be running two displays (every graphics card provides at least two outputs, with most motherboards also providing display output and cheap access to a second graphics card), it's now easy for everyone to enjoy three, four, six or even 10 displays.

The complete guide to multiple monitors

While many people might think it's simply unnecessary to have this many screens, triple-panel gaming and a host of demanding jobs require an amount of digital desktop space only a multi-monitor setup can provide. You might be surprised how low the entry requirements for multi-monitor setups are these days. Even the panels aren't that expensive if you opt for the cheaper TN-based displays, with 22-inch 1080p panels available for under £100 and the larger 24-inch 1080p versions costing just a little more. This means it's a great time to think about maxing out your monitors. Before contemplating additional monitors, the first step is to consider where they're going to go. You card Choose a driver Porting it about Desktop control. How to get gogo in flight wireless internet for free. Gogo’s new fancy logo Stealing internet is illegal.

I do not condone stealing of internet services. Login. BitTorrent Sync - Use Cases. Step 5 - Files are transferred directly between peers Each file contained in a Sync folder has an associated torrent and is broken up into small pieces before it is transferred. Using the BitTorrent protocol, this protocol allows devices to receive file pieces from any peer simultaneously. The more devices a folder is shared with, the faster files are transferred. While the speed is not visible to the user, the accelerated speeds will be noticeable. BitTorrent Sync - Developer API. Methods Note: Sync Secrets are now referred to as Keys; however, method calls in the API remain the same and are referred to as ‘Secrets’.

Please see 'Get Secrets' for documentation on how to use Secrets in the API. Get Folder Returns an array with folders info. If a secret is specified, will return info about the folder with this secret. Audiocogs/flac.js · GitHub. List of online music databases. Your External Hard Drive in the Cloud.

Django

Multiple Databases. Beets.