8169 Open source projects that are alternatives of or similar to Andrew Ng Notes

Material for my PyData Jupyter & Pandas Workshops, I'm also available for personal in-house trainings on request

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Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

Course materials for Dartmouth Course: Storytelling with Data (PSYC 81.09).

Big Data for Data Engineers Coursera Specialization from Yandex

Disease Prediction based on Symptoms.

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Reinforcement Learning Workshop for Data Science BKK

Solutions to assignments of Coursera Specializations - Deep learning, Machine learning, Algorithms & Data Structures, Image Processing and Python For Everybody

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

https://arxiv.org/abs/1707.00836

🤖⚡️ scikit-learn tips

50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

Notes and Assignments for Andrew Ng's Machine Learning - Python3 code

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Machine learning contest - October 2016 TLE

[Book-2019] Pragmatic AI: An Introduction to Cloud-based Machine Learning

Using A.I. and computer vision to build a virtual personal fitness trainer. (Most Startup-Viable Hack - HackNYU2018)

Reinforcement learning material, code and exercises for Udacity Nanodegree programs.

适合初级到中级晋升者，有了体系之后就看熟练度了。

TensorFlow Tutorials with YouTube Videos

Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4

Open Source Specialized Computing Stack for Accelerating Deep Neural Networks.

A PyTorch implementation of OpenAI's REPTILE algorithm

Hiding Images within other images using Deep Learning

SF Brigade's Data Science Working Group.

A constantly updated python machine learning cheatsheet

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Breast Cancer Classification using CNN and transfer learning

GitHub Action for repo2docker

Small Docker image with Python Machine Learning tools (~180MB) https://hub.docker.com/r/frolvlad/alpine-python-machinelearning/

Series of the problem 💯 and solution ✅ asked by Daily Coding problem👨‍🎓 website.

fklearn: Functional Machine Learning

Pytorch implementation of the DeepDream computer vision algorithm

danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

A Handful of D(u)S(t)

Curated list for Deep Reinforcement Learning (DRL): software frameworks, models, datasets, gyms, baselines...

Intro to Deep Learning, including recurrent, convolution, and feed forward neural networks.

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

PyMC3 codes of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Pratical Course

PyTorch tutorial for learners

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Projeto de análise de contratos com suspeita de superfaturamento e má qualidade na prestação de serviços.

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Best Practices on Recommendation Systems

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Open-source implementation of Google Vizier for hyper parameters tuning

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CNN image classifier implemented in Keras Notebook 🖼️.

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The Raspberry Turk is a robot that can play chess—it's entirely open source, based on Raspberry Pi, and inspired by the 18th century chess playing machine, the Mechanical Turk.

StarNet

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This is a repository which contains all my work related Machine Learning, AI and Data Science. This includes my graduate projects, machine learning competition codes, algorithm implementations and reading material.

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A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.