Getting Started With Superset: Airbnb’s data exploration platform
At the time of writing, Python v3.5 and PIP v9.0.1 were available on AWS EC2.
sudo yum update -y
sudo yum install python35 -y
import sys | |
import os | |
import cv2 | |
import numpy as np | |
import tensorflow as tf | |
sys.path.append("..") | |
from object_detection.utils import label_map_util |
At the time of writing, Python v3.5 and PIP v9.0.1 were available on AWS EC2.
sudo yum update -y
sudo yum install python35 -y
With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.
The following section is divided in to two parts. Caffe's documentation suggest
FROM jupyter/notebook | |
# Install nbgrader | |
RUN pip2 install nbgrader && pip3 install nbgrader | |
# Add any other dependencies you might want, e.g. numpy, scipy, etc. | |
#RUN pip2 install numpy scipy matplotlib | |
#RUN pip3 install numpy scipy matplotlib | |
# Configure grader user |