Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

<ul>
<li>Ease of use: Zipline tries to get out of your way so that you can
focus on algorithm development. See below for a code example.</li>
<li>Zipline comes "batteries included" as many common statistics like
moving average and linear regression can be readily accessed from
within a user-written algorithm.</li>
<li>Input of historical data and output of performance statistics are
based on Pandas DataFrames to integrate nicely into the existing
PyData eco-system.</li>
<li>Statistic and machine learning libraries like matplotlib, scipy,
statsmodels, and sklearn support development, analysis, and
visualization of state-of-the-art trading systems.</li>
</ul>

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

Zipline - A Pythonic Algorithmic Trading Library

Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build chatbots on Facebook, Slack, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram etc. Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.

Rasa - Create chatbots and voice assistants

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Build an app in a few lines of code with magically simple API. Adding a widget is the same as declaring a variable. No need to write a backend, define routes etc. Effortlessly share, manage, and collaborate on your apps directly from Streamlit. Apps are deployed directly from Github repo.

Streamlit - The fastest way to build data apps in Python

Apache TVM, a deep learning compiler that enables access to high-performance machine learning anywhere for everyone. TVM’s diverse community of hardware vendors, compiler engineers and ML researchers work together to build a unified, programmable software stack, that enriches the entire ML technology ecosystem and make it accessible to the wider ML community. TVM empowers users to leverage community-driven ML-based optimizations to push the limits and amplify the reach of their research and development, which in turn raises the collective performance of all ML, while driving its costs down.

TVM - Open deep learning compiler stack for cpu, gpu and specialized accelerators

Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.

Stanza - A Python NLP Library for Many Human Languages

Jina is a neural search framework that empowers anyone to build SOTA & scalable deep learning search applications in minutes. It helps to build solutions for indexing, querying, understanding multi-/cross-modal data such as video, image, text, audio, source code, PDF.

Jina - Cloud-native neural search framework for any kind of data

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.
 
<ul>
<li>Quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code.</li>
<li>Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge.</li>
<li>Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.</li>
<li>Easily improve/tune your bespoke models and data pipelines, or customize <cite>AutoGluon</cite> for your use-case.</li>
</ul>

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

AutoGluon - AutoML for Text, Image, and Tabular Data

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.
 
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
 
Ray Core provides a simple and flexible API for building and running your distributed applications. You can often parallelize single machine code with little to zero code changes.
 
With a Ray cluster you can deploy your workloads on AWS, GCP, Azure or on premise. You can also use Ray cluster managers to run Ray on your existing Kubernetes, YARN, or Slurm clusters.

Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Ray is a unified way to scale Python and AI applications from a single node to a cluster.


Ray - A unified framework for scaling AI and Python applications

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more. 

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows. 

Kur - Descriptive Deep Learning

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

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