- Labelling Scenes
- Robot Navigation
- Self-Driving Cars
- Body Recognition (Microsoft Kinect)
- Disease and Cancer Detection
- Facial Recognition
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, |
This is a description of how I write tech articles for various blogs. Hopefully someone else will find this useful as well.
When I begin writing a new article, I create a new [GitHub Gist][gist] for the article files. The Gist contains a file for the article text and code examples related to the article.
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# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set. | |
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0 | |
# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' | |
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. | |
# Similarly, for h * W_2 + b_2 | |
import tensorflow as tf | |
import numpy as np | |
from sklearn import datasets | |
from sklearn.model_selection import train_test_split |