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

https://medium.com/towards-data-science/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464

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