Code and files to go along with CS329s machine learning model deployment tutorial.
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Updated
Apr 17, 2021 - Jupyter Notebook
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Code and files to go along with CS329s machine learning model deployment tutorial.
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Classify movie posters by genre
Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
Text clustering with K-means and tf-idf
Machine learning notes that make your reading easy
The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.
Workshop on Deep Learning for Health and Life Sciences
Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.
Predict diabetes disease using a Logistic Regression with TensorFlow.js
Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.
Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.
딥러닝 with C++ 소스 코드
A machine repository for kick-starting Machine Learning in no time!
Machine Learning tutorials covering both traditional and deep learning models.
Supports de la conférence "Machine Learning pour tous avec python" présentée au Breizhcamp 2019
In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.
The repository contains exercises on Machine Learning algorithms in R, using RStudio. Used to dive into ML, data preprocessing, data visualisation, and data exploration.
Algotrading101 article about Sklearn
Machine learning case study
Machine Learning Algorithm Implementations
A follow up page for the session on Machine and Learning and Deep Learning frameworks at GNR 652 course.
Study notes for Elements of Statistical Learning (ESL) book.
A Basic tutorial for beginners in Data Science. Contains step by step solution on the Titanic Dataset.
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