#
kernelrich
Follow
Starred repositories
Examples of Data Science projects and Artificial Intelligence use-cases
The MNIST database of handwritten digits is one of the most popular image recognition datasets. It contains 60k examples for training and 10k examples for testing.
TensorFlow Tutorials with YouTube Videos
Keras documentation, hosted live at keras.io
This project demonstrates how you can develop simple calculator using HTML, CSS, and JavaScript.
Tutorials on machine learning, artificial intelligence, data science with math explanation and reusable code (in python and R)
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
A place in which we publish scripts for reproducible benchmarks.
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron)
A neural machine translator from english to spanish in tf2 using encoder-decoder with attention
A study on the ingenious concept of word2vec. The repository contains a detailed code of the CBOW and Skip-gram architectures.
Efficient tree-based convolutional neural networks in TensorFlow
A tool to convert nodes in an Abstract Syntax Tree into vector embeddings
MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.
Commented (but unaltered) version of original word2vec C implementation.
Jupyter notebooks authored by Richard Evans
C++ template library for high performance SIMD based sorting algorithms
Macros in Python: quasiquotes, case classes, LINQ and more!
Low-Budget Password Strength Estimation
Python Data Science Handbook: full text in Jupyter Notebooks
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
COMS W4995 Applied Machine Learning - Spring 20
Pyspark RDD, DataFrame and Dataset Examples in Python language
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.