A casual list of sources for Machine Learning, NLP and software engineering for ML, that I come back to or that simply caught my attention :)
- Lore: A python framework to make machine learning approachable for Engineers and maintainable for Data Scientists
- LabNotebook: A simple experiment manager for deep learning experiments
- DeepPavlov: Open-source conversational AI library built on TensorFlow and Keras
- Advisor: hyper parameters tuning system for black box optimization, opensource implementation of Google Vizier
- Michelangelo and PyML: Uber's machine learning platforms
- NLP Architect: Intel's AI Lab (Nirvana) Python library for exploring the state-of-the-art deep learning topologies for NLP
- MetaCar: A reinforcement learning environment for self-driving cars in the browser
- Kubeflow and Seldon-core - Machine Learning Toolkits for Kubernetes
- MlFlow An open source platform for the machine learning lifecycle (focused on Model serving and management)
- Bighead Airbnb’s End-to-End Machine Learning Platform
- Clipper ML-workflow platform, currently focused on low-latency prediction serving video here
- StudioML Model management framework with aim to simplify model building experience
- Data Infrastructure at Airbnb HIgh level description of Airbnb's data stack
- BOBP: The Best of the Best Practices (BOBP) Guide for Python
- Real Python: Learn Python by example
- Rules of Machine Learning: The Best Practices for ML Engineering by Martin Zinkevich from Google
- Text classification Guide by Google
- Good practices in Modern Tensorflow for NLP
- Refactoring GURU: Refactoring, Design Patterns, etc.
- Python Anti-patterns
- Good logging practice in Python: tips on using standard logging module
- Scaling Machine Learning at Uber with Michelangelo, and some ML organizational insights
- Writing Code for NLP Research by AllenAI
- A Full Hardware Guide to Deep Learning
- TensorFlow Model Deployment tutorial
- Tensorflow nmt: Neural Machine Translation (seq2seq) Tutorial
- Hyperparameter optimization for keras with talos by Mikko on Medium
- How to write simple Spelling Corrector from Peter Norvig
- Kubernetes-GPU-Guide - how to set GPU-enabled cluster on multiple GPU machines
- Feature Selection with sklearn and Pandas - Introduction to Feature Selection methods (Filter, Wrapper and embedded) and their implementation in Python
- Exploratory analysis in Pandas based on New York Taxi Fare Prediction problem on KAggle
- easy-tf-log Simplifies logging in Tensorflow/Tensorboard
- Tune Scalable framework for hyperparameter search with a focus on deep learning
- AdaNet A lightweight and scalable TensorFlow AutoML framework
- TFMA Model analysis tools for TensorFlow
- What-if-tool And interface for expanding understanding of classification or regression ML mode (Tensorflow)
- TRFL Reinforcement Learning library based on Tensorflow
- pyLDAvis Python library for interactive topic model visualization
- Uber's Ludvig An extensible toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code (Infers appropriate model from data schema in Pandas)
- vue.js: A progressive Javascript playbook
- Deep AI Playbook: Strategy for Disruptive Artificial Intelligence
- Python Cookbook by O'Reilly
- Mathematics for Machine Learning to be published by Cambridge University Press
- Deep Learning Book by Bengio, Goodfellow and Courville
- Devops for Data Science on LinkedIn learning
- Free Deep RL course/blog by Thomas Simonini
- Deep Learning course from Stanford, with Andrew Ng! ;)
- Deep Learning for NLP course from Stanford with Richard Socher (lecture videos from past years can be found on youtube)
- Neural Nets for NLP from CMU, only lecture slides
- ML with Tensorflow on GCP at Coursera
- Fast AI course on Deep Learning
- OpenAI Spinning up Not really a course. Rather educational resource produced by OpenAI about Deep RL
- Sebastian Ruder
- Kumar Shridhar (Botsupply)
- nlp-datasets - Alphabetical list of free NLP data
- awesome-nlp - A curated list of resources dedicated to Natural Language Processing (NLP)
- Data Science Process Management Tips ;)