Machine Learning/scratch notes

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ML notes

Jupyter Notebook
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jupyter notebook --no-browser --ip --port 5000
jupyter notebook --no-browser --ip --port 5000 --log-level=DEBUG

inline image:

possibly this:

%matplotlib inline
possibly this:

from IPython.display import Image
To Read

Machine Learning
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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 - in progress

course: at coursera

understanding machine learning theory algorithms

random forest
Nearest Neighbors Classification
python + libs

Caffe deep learning framework
SystemML- a Universal Translator for Big Data and Machine Learning
image labeling
TensorFlow Playground
sample data


Cool Projects

Aerospace Controls Lab
Data leaks
When data associated iwth the data set gives away the target data.

Primarily of concern in competition.

Unexpected data.


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:
What a Deep Neural Network thinks about your #selfie
Detecting tanks
Kaggle competitions:
University of Toronto Machine Learning
Past solutions

NIPS - Neural Information Processing Systems
Demos and Labs

Jaz Quick start
use your GPU / TPU for ML:
Image processing
Christopheraburns / gluoncv-yolo-playing_cards

Improving our neural network (96% MNIST)

linear regression in 6 lines of code

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, Y)  # perform linear regression
Y_pred = linear_regressor.predict(X)  # make predictions
plt.scatter(X, Y)
plt.plot(X, Y_pred, color='red')
Category: Data Science
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This page was last edited on 29 February 2020, at 02:59.Privacy policyAbout Federal Burro of InformationDisclaimers

D3 visualizations in jupyter notebooks: