Machine Learning/scratch notes
ML notes
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jupyter notebook --no-browser --ip 0.0.0.0 --port 5000 jupyter notebook --no-browser --ip 0.0.0.0 --port 5000 --log-level=DEBUG
inline image:
possibly this:
%matplotlib inline
possibly this:
from IPython.display import Image
To Read: https://voila.readthedocs.io/en/stable/index.html
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Contents 1 getting started 2 algorithms 3 tools 4 sample data 5 blogs 6 Cool Projects 7 Data leaks 8 Questions and Investigation 9 Reading Room 9.1 NIPS - Neural Information Processing Systems 10 Demos and Labs 11 Image processing 12 Chapter 13 linear regression in 6 lines of code
getting started google://getting started with machine learning
https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience - in progress
https://www.quora.com/I-want-to-learn-machine-learning-Where-should-I-start
http://thunderboltlabs.com/blog/2013/11/09/getting-started-with-machine-learning/
http://machinelearningmastery.com/machine-learning-for-programmers/
https://www.kaggle.com/dfernig/reddit-comments-may-2015/the-biannual-reddit-sarcasm-hunt/code
course: at coursera https://www.coursera.org/learn/machine-learning/home/week/1
understanding machine learning theory algorithms
algorithms random forest https://medium.com/rants-on-machine-learning/the-unreasonable-effectiveness-of-random-forests-f33c3ce28883 Nearest Neighbors Classification http://scikit-learn.org/stable/modules/neighbors.html lstm http://blog.echen.me/2017/05/30/exploring-lstms/ tools python + libs
Caffe deep learning framework SystemML- a Universal Translator for Big Data and Machine Learning http://www.ibm.com/blogs/think/2015/11/24/introducing-a-universal-translator-for-big-data-and-machine-learning/ https://github.com/SparkTC/systemml/ http://researcher.watson.ibm.com/researcher/view_group.php?id=3174 https://developer.ibm.com/open/systemml/ image labeling
https://github.com/Labelbox/Labelbox TensorFlow Playground
http://playground.tensorflow.org sample data http://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions
blogs http://blog.datumbox.com/
Cool Projects https://github.com/aficnar/slackpolice
Aerospace Controls Lab
http://acl.mit.edu/
https://www.youtube.com/channel/UCVTxuaJsdMrk3UEcHVll9Yg
Data leaks
When data associated iwth the data set gives away the target data.
Primarily of concern in competition.
Unexpected data.
refrence: https://www.coursera.org/learn/competitive-data-science/lecture/5w9Gy/basic-data-leaks
Future peaking - using time series data that's not in the target time period, for example in the future.
Meta data leaks - for example file meta data, zip file meta data, image file meta data.
information hidden in ID and hashes,
and information hidden in row order and possibly duplicate rows
Questions and Investigation What are "ground truths"?
corteges - what is this word
/Courera's Competitive Data Science Course
Reading Room an good overview the the data science cycle in a general sense: https://cloud.google.com/ml-engine/docs/tensorflow/data-prep What a Deep Neural Network thinks about your #selfie Detecting tanks https://www.jefftk.com/p/detecting-tanks https://analyticsdefined.com/mining-enron-emails/ https://www.coursera.org/learn/competitive-data-science/lecture/5w9Gy/basic-data-leaks https://opendatascience.com/blog/ Kaggle competitions: https://www.kaggle.com/ University of Toronto Machine Learning http://www.learning.cs.toronto.edu/theses.html Past solutions
http://ndres.me/kaggle-past-solutions/ https://www.kaggle.com/wiki/PastSolutions http://www.chioka.in/kaggle-competition-solutions/ https://github.com/ShuaiW/kaggle-classification/ https://towardsdatascience.com/how-to-use-dataset-in-tensorflow-c758ef9e4428
https://towardsdatascience.com/how-to-train-neural-network-faster-with-optimizers-d297730b3713
NIPS - Neural Information Processing Systems 2015 https://nips.cc/Conferences/2015 2016 https://nips.cc/Conferences/2016 Demos and Labs https://codelabs.developers.google.com/codelabs/scd-babyweight2/index.html#0
https://github.com/GoogleCloudPlatform/training-data-analyst
Jaz Quick start use your GPU / TPU for ML: https://jax.readthedocs.io/en/latest/notebooks/quickstart.html https://github.com/cbrownley/foundations-for-analytics-with-python Image processing Christopheraburns / gluoncv-yolo-playing_cards https://github.com/Christopheraburns/gluoncv-yolo-playing_cards/blob/master/Yolov3.ipynb Chapter https://github.com/FlorianMuellerklein/Machine-Learning
Improving our neural network (96% MNIST) https://databoys.github.io/ImprovingNN/
https://iamtrask.github.io/2015/07/12/basic-python-network/
https://plot.ly/python/create-online-dashboard/
https://www.anaconda.com/download/
http://jupyter.org/install.html
linear regression in 6 lines of code source: https://towardsdatascience.com/linear-regression-in-6-lines-of-python-5e1d0cd05b8d
pip install scikit-learn
import numpy as np
import matplotlib.pyplot as plt # To visualize
import pandas as pd # To read data
from sklearn.linear_model import LinearRegression
data = pd.read_csv('data.csv') # load data set
X = data.iloc[:, 0].values.reshape(-1, 1) # values converts it into a numpy array
Y = data.iloc[:, 1].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression() # create object for the class
linear_regressor.fit(X, Y) # perform linear regression
Y_pred = linear_regressor.predict(X) # make predictions
plt.scatter(X, Y)
plt.plot(X, Y_pred, color='red')
plt.show()
D3 visualizations in jupyter notebooks: https://medium.com/@stallonejacob/d3-in-juypter-notebook-685d6dca75c8
