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View imagenet_classes.txt
List of ImageNet class numbers and names as used in Keras' pre-trained models.
Extracted from https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
0, tench
1, goldfish
2, great_white_shark
3, tiger_shark
4, hammerhead
5, electric_ray
View dlib and face_recognition on raspberry pi.md

Install dlib and face_recognition on a Raspberry Pi

Instructions tested with a Raspberry Pi 2 with an 8GB memory card. Probably also works fine on a Raspberry Pi 3.

Steps

Download the latest Raspbian Jessie Light image. Earlier versions of Raspbian won't work.

Write it to a memory card using Etcher, put the memory card in the RPi and boot it up.

View installing_dlib_on_macos_for_python.md

How to install dlib v19.9 or newer (w/ python bindings) from github on macOS and Ubuntu

Pre-reqs:

  • Have Python 3 installed. On macOS, this could be installed from homebrew or even via standard Python 3.6 downloaded installer from https://www.python.org/download. On Linux, just use your package manager.
  • On macOS:
    • Install XCode from the Mac App Store (or install the XCode command line utils).
    • Have homebrew installed
  • On Linux:
View face detection system example.md

Before you start

Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:

docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface
View Build tensorflow on OSX with NVIDIA CUDA support.md

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

Requirements

OS X 10.10 (Yosemite) or newer

View gist:c40fba50b6fece4ee1e7
import numpy as np
from sklearn import grid_search
from sklearn import cross_validation
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
# Load your starting coefficient data as an array called X. This can be as big
# as your computer's memory. If that's still not big enough, you can load it in
# segments and use partial_fit instead of fit.
View gist:1d85f8482953be37d9c3
from sklearn import linear_model
X = [
[4,3,1,0,1],
[5,2,1,0,1],
[4,2,1,1,1],
[3,1,0,1,1],
[1,1,0,1,1]
]
View gist:34f49d956a60b781f947
X = [4 3 1 0 1; 5 2 1 0 1; 4 2 1 1 1; 3 1 0 1 1; 1 1 0 1 1]
y = [4, 6, 6, 3, 1]
theta = pinv(X' * X) * X' * y'
View estimate.py
def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0
# In my area, the average house costs $200 per sqft
price_per_sqft = 200
if neighborhood == "hipsterton":
# some areas cost a bit more
price_per_sqft = 400
elif neighborhood == "skid row":
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