kantapithm Goto Github PK

docs

Documentation for ASP.NET Core

dsc-correlation-covariance-nyc-ds-career-042219

easypki

Creating a certificate authority the easy way

epl-prediction-2017

Predicting weekly football results in EPL for the 2017 season

football_outcome_prediction

freelearningresourcesforsoftwaretesters

A New Project to create a set of links to free Online Learning Resources for New and Experienced Software Testers.

highcharts

Highcharts JS, the JavaScript charting framework

how-to-make-dashboards-with-tableau

intelligent-mobile-projects-with-tensorflow

Intelligent Mobile Projects with TensorFlow, published by Packt

irfcm

Improved Relational Fuzzy c-Means (iRFCM)

it-ebooks

keras-retinanet

Keras implementation of RetinaNet object detection.

machinelearning

Machine Learning Key Projects

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

microsoft-graph-docs

Documentation for the Microsoft Graph REST API

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

objectdetectionusingcntk

Fast R-CNN Object Detection on Azure using CNTK

open-solution-home-credit

Open solution to the Home Credit Default Risk challenge :house_with_garden:

openvino-yolov3

YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO

programmingtest

python-script-037-venn-diagram

pythondatasciencehandbook

Jupyter Notebooks for the Python Data Science Handbook

ragnarokcontent

Link to all my Ragnarok Content (scripts, db, maps...)

santander-product-recommendation

seaborn-tutorial

This repository is my attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random datasets.

siametrics-pre-interview-assignment

simple-blog-part-1-build

Create a blog from scratch with PHP

simple-venn

Simple venn diagrams in Python

siraj_course_how-to-do-linear-regression-using-gradient-descent

# linear_regression_live This is the code for the "How to Do Linear Regression the Right Way" live session by Siraj Raval on Youtube ## Overview This is the code for [this](https://youtu.be/uwwWVAgJBcM) video on Youtube by Siraj Raval. I'm using a small dataset of student test scores and the amount of hours they studied. Intuitively, there must be a relationship right? The more you study, the better your test scores should be. We're going to use [linear regression](https://onlinecourses.science.psu.edu/stat501/node/250) to prove this relationship. Here are some helpful links: #### Gradient descent visualization https://raw.githubusercontent.com/mattnedrich/GradientDescentExample/master/gradient_descent_example.gif #### Sum of squared distances formula (to calculate our error) https://spin.atomicobject.com/wp-content/uploads/linear_regression_error1.png #### Partial derivative with respect to b and m (to perform gradient descent) https://spin.atomicobject.com/wp-content/uploads/linear_regression_gradient1.png ## Dependencies * numpy Python 2 and 3 both work for this. Use [pip](https://pip.pypa.io/en/stable/) to install any dependencies. ## Usage Just run ``python3 demo.py`` to see the results: ``` Starting gradient descent at b = 0, m = 0, error = 5565.107834483211 Running... After 1000 iterations b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473 ``` ## Credits Credits for this code go to [mattnedrich](https://github.com/mattnedrich). I've merely created a wrapper to get people started.

snake-game