andhau's repositories
pugs-start-up
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Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
Introduction-to-JSX
Created with CodeSandbox
cledesign
Mockup
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