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awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome-text-classification

Awesome-Text-Classification Projects,Papers,Tutorial .

bash-cheat-sheet

A cheat sheet for bash commands.

bert

TensorFlow code and pre-trained models for BERT

cnn-explainer

Learning Convolutional Neural Networks with Interactive Visualization.

corenlp

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

course-nlp

A Code-First Introduction to NLP course

courseramachinelearning

Coursera Machine Learning By Prof. Andrew Ng

deeplearning-notebooks

Deep Learning Jupyter Notebooks

deepreinforcementlearninginaction

Code from the Deep Reinforcement Learning in Action book from Manning, Inc

deepsparse

Sparsity-aware deep learning inference runtime for CPUs

deployment_v2

second trial deployment

docker-jenkins-flask

dragin

DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models

evidently

Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

face-recognition

facenet

Face recognition using Tensorflow

fashion-mnist

A MNIST-like fashion product database. Benchmark :point_down:

fasttext

Library for fast text representation and classification.

jenkins-example-docker

ktrain

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

langchain

🦜🔗 Build context-aware reasoning applications

lime

Lime: Explaining the predictions of any machine learning classifier

llamaindex_gradio_example

An example of using Llamaindex and gradio to perform Q&A over a pdf

machine_learning_from_scratch

machinelearning

Machine learning and artificial intelligence

mit-6.s094

MIT-6.S094: Deep Learning for Self-Driving Cars Assignments solutions

mit-deep-learning-book-pdf

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

ml-in-production-airflow

The practical use-cases of how to make your Machine Learning Pipelines robust and reliable using Apache Airflow.

ml-ops-ci-experiment