Ruben Stefanus's Projects
:memo: An awesome Data Science repository to learn and apply for real world problems.
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
Data Science Cheatsheet and NLP Road Map
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
A Code-First Introduction to NLP course
Credit Scoring Project
Design Patterns for Data Science
code for Data Science From Scratch book
Repo for the Deep Learning Nanodegree Foundations program.
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
Docker template for Machine Learning development in container
example dvc versioning
Repo for principles, job bands, coding challenges, and anything else we're ok sharing w/ the world.
The Effect of Training Video Number for Each Class in Near Duplicate Video Retrieval Application using t-Distributed Unsupervised Stochastic Multi-View Hashing Method
The Google Cloud Developer's Cheat Sheet
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.
Image Classification, LeNet-5 CNN Architecture, CIFAR10 Dataset
Indonesian BERT Fine Tuning News Classification
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
Code Samples from Neural Networks for NLP
A repository of concepts related to neural networks for NLP
PyTorch Lightning for MNIST
Design and Realization of Practicum Participants Attendance Detector using RFID Based on IOT
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 ;)