Overview — Pylearn2 dev documentation
This page gives a high-level overview of the Pylearn2 library and describes how the various parts fit together. First, before learning Pylearn2 it is imperative that you have a good understanding of Theano. Before learning Pylearn2 you should first understand: How Theano uses Variables, Ops, and Apply nodes to represent symbolic expressions.What a Theano function is.What Theano shared variables are and how they can make state persist between calls to Theano functions. Once you have that under your belt, we can move on to Pylearn2 itself. Note that throughout this page we will mention several different classes and functions but not completely describe their parameters.

Torch vs Theano
Recently we took a look at Torch 7 and found its data ingestion facilities less than impressive. Torch’s biggest competitor seems to be Theano, a popular deep-learning framework for Python. These two have been having “who is faster” competition going for a few years now.
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. In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.

Reaching Enlightenment I.
Cid’s brain is an instance of a Deep Boltzmann Machine (DBM). In a DBM, the connections amongst the visible and hidden units have a particular structure. This structure removes connections from a fully connected model such that layers in the network can be naturally defined. In a DBM, each layer has no connections amongst its units. Each unit in a layer is connected to every unit in both the layers immediately above and immediately below. The DBM type structure is the middle one in the picture here.

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! 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.

What is Deep Learning?
Scyfer is a University of Amsterdam spinoff that specializes in deep learning technology. We build deep neural network solutions for image and speech recognition as well as for recommender systems. But what is deep learning? Deep Learning Deep learning is a subfield within machine learning that deals with developing efficient training algorithms for deep neural networks.
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. 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.

UFLDL Tutorial - Ufldl
From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

Redis as the primary data store? WTF?!
The web is abound with warnings and cautionary tales about going this route. 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.
mechanize
Stateful programmatic web browsing in Python, after Andy Lester’s Perl module WWW::Mechanize. The examples below are written for a website that does not exist (example.com), so cannot be run. There are also some working examples that you can run. import reimport mechanize

Observable
In ReactiveX an observer subscribes to an 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. So you're curious in learning this new thing called Reactive Programming, particularly its variant comprising of Rx, Bacon.js, RAC, and others.
Open-source, distributed deep learning for the JVM
Deeplearning4j is not the first open-source deep-learning project, but it is distinguished from its predecessors in both programming language and intent. DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework intended to solve problems involving massive amounts of data in a reasonable amount of time. It integrates with Hadoop and Spark using an arbitrary number of GPUs or CPUs, and it has a number you can call if anything breaks. Get Started With Deeplearning4j Content