andhau

pugs-start-up

Sign up for treats

andhau-math-garden

Financial-Models-Numerical-Methods

Collection of notebooks about quantitative finance, with interactive python code.

Introduction-to-JSX

Created with CodeSandbox

cledesign

Mockup

pugs

wordcount

Test GitHub

mindsdb

Framework to streamline use of neural networks

data

Data and code behind the articles and graphics at FiveThirtyEight

tensorwatch

Debugging, monitoring and visualization for Python Machine Learning and Data Science

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

deeplearning-models

A collection of various deep learning architectures, models, and tips

Data-Engineering-HowTo

A list of useful resources to learn Data Engineering from scratch

WolframClientForPython

Call Wolfram Language functions from Python

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

challenges

PyBites Code Challenges

docs

TensorFlow documentation

keras

Deep Learning for humans

100daysofcode-with-python-course

Course materials and handouts for #100DaysOfCode in Python course

datacamp-community-tutorials

Tutorials for DataCamp (www.datacamp.com)

datasets

A collection of datasets ready to use with TensorFlow

Machine-Learning

Python implementation of machine learning algorithms

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Machine-Learning-with-Python

Python code for common Machine Learning Algorithms

MediumRare

In this repo I will upload all code related to my posts on medium or any other platform

kaggle-api

Official Kaggle API

machine_learning_examples

A collection of machine learning examples and tutorials.

jupyter-themes

Custom Jupyter Notebook Themes

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

Data-Analysis

Data Analysis Using Python