turtlemonvh

python -c 'import site; print(",".join(site.getsitepackages()))' | tr , '\n' | xargs -I {} find {} -name easy-install.pth

fg91

class FilterVisualizer():
    def __init__(self, size=56, upscaling_steps=12, upscaling_factor=1.2):
        self.size, self.upscaling_steps, self.upscaling_factor = size, upscaling_steps, upscaling_factor
        self.model = vgg16(pre=True).cuda().eval()
        set_trainable(self.model, False)

    def visualize(self, layer, filter, lr=0.1, opt_steps=20, blur=None):
        sz = self.size
        img = np.uint8(np.random.uniform(150, 180, (sz, sz, 3)))/255  # generate random image
        activations = SaveFeatures(list(self.model.children())[layer])  # register hook

anishathalye

bluekvirus

How To Serve Flask Applications with uWSGI and Nginx on Ubuntu 14.04

@credit Yan Zhu (https://github.com/nina-zhu)

Introduction

Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions, it can help you get your Python application or website off the ground. Flask includes a simplified development server for testing your code locally, but for anything even slightly production related, a more secure and powerful web server is required.

In this guide, we will demonstrate how to install and configure some components on Ubuntu 14.04 to support and serve Flask applications. We will configure the uWSGI application container server to interface with our applications. We will then set up Nginx to reverse proxy to uWSGI, giving us access to its security and performance features to serve our apps.

Prerequisites and Goals

bigsnarfdude

General Nanodegree Information
Nanodegrees Programs: https://www.udacity.com/nanodegree
Nanodegree Plus (job guarantee): https://www.udacity.com/nanodegree/plus 
UConnect (weekly in-person study sessions):  https://www.udacity.com/uconnect 

Courses on Udacity
Machine Learning Engineer Nanodegree by Google (Currently Available): https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009
Artificial Intelligence for Robots (Free Course) https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373
Intro to Statistics (Free Course) https://www.udacity.com/course/intro-to-statistics--st101
Deep Learning (Free Course) https://www.udacity.com/course/deep-learning--ud730

shagunsodhani

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.

kylemcdonald

Theory of Building Caffe on OS X

Introduction

Our goal is to run python -c "import caffe" without crashing. For anyone who doesn't spend most of their time with build systems, getting to this point can be extremely difficult on OS X. Instead of providing a list of steps to follow, I'll try to explain why each step happens.

This page has OS X specific install instructions.

I assume:

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

bogdanr

visible_hostname vm-gateway
#
# Recommended minimum configuration:
#

# Example rule allowing access from your local networks.
# Adapt to list your (internal) IP networks from where browsing
# should be allowed
acl localnet src 10.10.10.0/24
acl localnet src 127.0.0.1