| gitflow | git |
|---|---|
git flow init |
git init |
git commit --allow-empty -m "Initial commit" |
|
git checkout -b develop master |
(by @andrestaltz)
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.
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| """ | |
| A deep neural network with or w/o dropout in one file. | |
| License: Do What The Fuck You Want to Public License http://www.wtfpl.net/ | |
| """ | |
| import numpy, theano, sys, math | |
| from theano import tensor as T | |
| from theano import shared | |
| from theano.tensor.shared_randomstreams import RandomStreams |
- Courses and MOOCs
- Introduction to Convex Optimization MITOCW by Prof. Stephen Boyd
- Convex Analysis and Optimization MITOCW by Prof. Dimitri Bertsekas
- Convex Optimization MOOC from Stanford Online
- Convex Optimization at CMU Spring 2015
- Books
- Convex Optimization – Boyd and Vandenberghe - downloadable book
- Convex Optimization Theory - by Dimitri P. Bertsekas
- Theory of Convex Optimization for Machine Learning - download
- Feature Learning
- Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
- The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
- Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
- Scaling Learning Algorithms towards AI Benjio & LeCun
- A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson
- Deep Learning
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
- [Understanding
Kibana 3 against ElasticSearch 1.4 throws an Connection Failed screen. The error text says to set http.cors.allow-origin, but it misses out the important http.cors.enabled: true
Working config:
$ grep cors elasticsearch-1.4.0.Beta1/config/elasticsearch.yml
http.cors.allow-origin: "/.*/"
http.cors.enabled: true
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| import com.twitter.algebird.{Aggregator, Semigroup} | |
| import com.twitter.scalding._ | |
| import scala.util.Random | |
| /** | |
| * This job is a tutorial of sorts for scalding's Execution[T] abstraction. | |
| * It is a simple implementation of Lloyd's algorithm for k-means on 2D data. | |
| * | |
| * http://en.wikipedia.org/wiki/K-means_clustering |
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| import theano | |
| import theano.tensor as T | |
| from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams | |
| from theano.tensor.signal.downsample import max_pool_2d | |
| from theano.tensor.extra_ops import repeat | |
| from theano.sandbox.cuda.dnn import dnn_conv | |
| from time import time | |
| import numpy as np | |
| from matplotlib import pyplot as plt |
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| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
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| # Authors: Kyle Kastner | |
| # License: BSD 3-clause | |
| import theano.tensor as T | |
| import numpy as np | |
| import theano | |
| class rmsprop(object): | |
| """ | |
| RMSProp with nesterov momentum and gradient rescaling |