data-science-from-scratch | code for Data Science From Scratch book | Learning library

by joelgrus Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | data-science-from-scratch Summary

data-science-from-scratch is a Python library typically used in Tutorial, Learning applications. data-science-from-scratch has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However data-science-from-scratch has 12 bugs. You can download it from GitHub.

code for Data Science From Scratch book

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Quality

Security

License

Reuse

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data-science-from-scratch has a medium active ecosystem.

It has 7648 star(s) with 4182 fork(s). There are 633 watchers for this library.

It had no major release in the last 6 months.

There are 54 open issues and 26 have been closed. On average issues are closed in 110 days. There are 25 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of data-science-from-scratch is current.

Quality

data-science-from-scratch has 12 bugs (0 blocker, 0 critical, 12 major, 0 minor) and 500 code smells.

Security

data-science-from-scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

data-science-from-scratch code analysis shows 0 unresolved vulnerabilities.

There are 112 security hotspots that need review.

License

data-science-from-scratch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

data-science-from-scratch releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

data-science-from-scratch saves you 4781 person hours of effort in developing the same functionality from scratch.

It has 10084 lines of code, 986 functions and 85 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed data-science-from-scratch and discovered the below as its top functions. This is intended to give you an instant insight into data-science-from-scratch implemented functionality, and help decide if they suit your requirements.

Make scatterplot matrix
Get the column of a matrix
Return the size of a tensor
Generate a random normal distribution
Build a tree id3
Compute the partition entropy of the input list
Group rows into a dict
Partition input by attribute
Get shortest paths from from from to_user
Estimates the least squares fit
Back - - propagation
Return a sorted list of suggested suggestions
Classify and plot a grid and plot it
Make plot of the variance of the model
Make graph dot product projection
Compute the entropy of the input array
Minimize an error function
Predict probability for a given text
Plots cities for each language
Train and test a model
Join two tables
Compute the squared gradient of the squared error vector
Groups the table according to the aggregation function
Compute the PageRank for each user
Create a new Table with columns and columns
Find the bottom - up cluster of inputs
Minimize stochastic stochastic

Get all kandi verified functions for this library.

data-science-from-scratch Key Features

No Key Features are available at this moment for data-science-from-scratch.

data-science-from-scratch Examples and Code Snippets

Bayes Email Spam Filter

Kotlin

Lines of Code : 19

License : Permissive (Apache-2.0)

Copy

Score for an email containing message: "discount viagra wholesale, hurry while this offer lasts"
0.9990090904079181

Score for an email containing message: "interesting meeting on amazon cloud services discount program"
0.01754275128116032

Spammiest

Data-Science-From-Scratch

Python

Lines of Code : 13

License : No License

Copy

cat someFile.txt | python egrep.py "[0-9]" | python line_count.py 

source_code_of_a_webpage = BeautifulSoup(requests.get(url_of_page).text,'html5lib')

import json
deserialized = json.loads(serialized_json)

Web-Scraping/twitter.py

twitter.py CONSU

Community Discussions

Trending Discussions on data-science-from-scratch

variable visibility/scope in python code that implements an ANN

QUESTION

variable visibility/scope in python code that implements an ANN

Asked 2018-May-24 at 14:45

I am self-implementing an artificial neural network (ANN) using an example code of [1]. While it is in principle clear to me how the ANN code works (I have done it in other languages before) I have more a problem with the python syntax/logic: In line 181 the network is trained in 10 000 interations but how is the progress saved because the function "backpropagate" (line 39) does not return the network and the variable "network" seems also not to be global variable? Also in the function "backpropagate" the variable "network" is not updated but I guess this is because the running variables such as "output_neuron" (line 48) are by reference? But that still does not explain how "network" saves its progress outside of "backpropagate"?

[1] https://github.com/joelgrus/data-science-from-scratch/blob/master/code-python3/neural_networks.py

...

ANSWER

Answered 2018-May-24 at 14:44

You should probably start with more basic code.

This demonstrates what happens

Source https://stackoverflow.com/questions/50511612

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install data-science-from-scratch

You can download it from GitHub.
You can use data-science-from-scratch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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