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Awesome Datascience

by academic ID: gh-tool--academic--awesome-datascience

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Open Source 28.3K Stars

1.0.0 Version

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Tool Information Summary
Entity Passport
Registry ID	gh-tool--academic--awesome-datascience
Provider	github

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Cite this tool

Academic & Research Attribution

BibTeX

@misc{gh_tool__academic__awesome_datascience,
  author = {academic},
  title = {Awesome Datascience Tool},
  year = {2026},
  howpublished = {\url{https://github.com/academic/awesome-datascience}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

academic. (2026). Awesome Datascience [Tool]. Free2AITools. https://github.com/academic/awesome-datascience

🔬Technical Deep Dive

Full Specifications [+]

⚡ Quick Commands

🐙 GitHub Clone

git clone https://github.com/academic/awesome-datascience

🐍 PIP Install

pip install awesome-datascience

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0.0

Top 19% Overall Impact

🔥 Popularity (P) 0

🚀 Velocity (V) 0

🛡️ Credibility (C) 0

🔧 Utility (U) 0

Nexus Verified Data

💬 Why this score?

The Nexus Index for Awesome Datascience aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

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📊 P: HuggingFace Stats 📈 V: 7-Day Delta 📄 C: Papers With Code 🔧 U: Deploy Score

Data Verified 🕐 Last Updated: Not calculated

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📋 Specs

Language: Python
License: Open Source
Version: 1.0.0

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Usage documentation not yet indexed for this tool.

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Technical Documentation

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AWESOME DATA SCIENCE

An open-source Data Science repository to learn and apply concepts toward solving real- world problems.

This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science, and what should I study to learn Data Science?"

Sponsor	Pitch
---	Be the first to sponsor! `[email protected]`

Link	Preview
Data Science For Beginners	Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science.
What is Data Science @ O'reilly	Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
What is Data Science @ Quora	Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century	Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia	Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist	Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience	The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.
Software Development Resources for Data Scientists	Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools.
Data Scientist Roadmap	Data science is an excellent career choice in today’s data-driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth.
Navigating Your Path to Becoming a Data Scientist	_Data science is one of the most in-demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _

Link	Description
The Data Science Lifecycle Process	The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo
Data Science Lifecycle Template Repo	Template repository for data science lifecycle project
RexMex	A general purpose recommender metrics library for fair evaluation.
ChemicalX	A PyTorch based deep learning library for drug pair scoring.
CorpusExplorer	Software for corpus linguists and text/data mining enthusiasts. Build your own corpora in over 60 languages. Use over 50 tools/visualizations.
PyTorch Geometric Temporal	Representation learning on dynamic graphs.
Little Ball of Fur	A graph sampling library for NetworkX with a Scikit-Learn like API.
Karate Club	An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
ML Workspace	All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)
Neptune.ai	Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
steppy	Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
steppy-toolkit	Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
Datalab from Google	easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.
Hortonworks Sandbox	is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
R	is a free software environment for statistical computing and graphics.
Tidyverse	is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
RStudio	IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux.
Python - Pandas - Anaconda	Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI	Pandas GUI
Scikit-Learn	Machine Learning in Python
NumPy	NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex	Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy	SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox	Coursera Course
Data Science Toolbox	Blog
Wolfram Data Science Platform	Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language.
Datadog	Solutions, code, and devops for high-scale data science.
Variance	Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit	The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs	Run, scale, share, and deploy your models — without any infrastructure or setup.
Apache Flink	A platform for efficient, distributed, general-purpose data processing.
Apache Hama	Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka	Weka is a collection of machine learning algorithms for data mining tasks.
Octave	GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark	Lightning-fast cluster computing
Hydrosphere Mist	a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics	A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe	Deep Learning Framework
Torch	A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework	Intel® Nervana™ reference deep learning framework committed to best performance on all hardware.
Skale	High performance distributed data processing in NodeJS
Aerosolve	A machine learning package built for humans.
Intel framework	Intel® Deep Learning Framework
Datawrapper	An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com
Tensor Flow	TensorFlow is an Open Source Software Library for Machine Intelligence
Natural Language Toolkit	An introductory yet powerful toolkit for natural language processing and classification
Annotation Lab	Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents.
nlp-toolkit for node.js	This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step.
Julia	high-level, high-performance dynamic programming language for technical computing
IJulia	a Julia-language backend combined with the Jupyter interactive environment
Apache Zeppelin	Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
Featuretools	An open source framework for automated feature engineering written in python
Optimus	Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
Albumentations	А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
DVC	An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files.
Lambdo	is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation.
Feast	A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Polyaxon	A platform for reproducible and scalable machine learning and deep learning.
UBIAI	Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
Trains	Auto-Magical Experiment Manager, Version Control & DevOps for AI
Hopsworks	Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale.
MindsDB	MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
Lightwood	A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
AWS Data Wrangler	An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc).
Amazon Rekognition	AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.
Amazon Textract	Automatically extract printed text, handwriting, and data from any document.
Amazon Lookout for Vision	Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.
Amazon CodeGuru	Automate code reviews and optimize application performance with ML-powered recommendations.
CML	An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests.
Dask	An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
Statsmodels	A Python-based inferential statistics, hypothesis testing and regression framework
Gensim	An open-source library for topic modeling of natural language text
spaCy	A performant natural language processing toolkit
Grid Studio	Grid studio is a web-based spreadsheet application with full integration of the Python programming language.
Python Data Science Handbook	Python Data Science Handbook: full text in Jupyter Notebooks
Shapley	A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
DAGsHub	A platform built on open source tools for data, model and pipeline management.
Deepnote	A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
Valohai	An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
PyMC3	A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
PyStan	Python interface to Stan (Bayesian inference and modeling)
hmmlearn	Unsupervised learning and inference of Hidden Markov Models
Chaos Genius	ML powered analytics engine for outlier/anomaly detection and root cause analysis
Nimblebox	A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser.
Towhee	A Python library that helps you encode your unstructured data into embeddings.
LineaPy	Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines.
envd	🏕️ machine learning development environment for data science and AI/ML engineering teams
Explore Data Science Libraries	A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources
MLEM	🐶 Version and deploy your ML models following GitOps principles
MLflow	MLOps framework for managing ML models across their full lifecycle
cleanlab	Python library for data-centric AI and automatically detecting various issues in ML datasets
AutoGluon	AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data
Arize AI	Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift.
Aureo.io	Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data.
ERD Lab	Free cloud based entity relationship diagram (ERD) tool made for developers.
Arize-Phoenix	MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models.
Comet	An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production.
Opik	Evaluate, test, and ship LLM applications across your dev and production lifecycles.
Synthical	AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place
teeplot	Workflow tool to automatically organize data visualization output
Streamlit	App framework for Machine Learning and Data Science projects
Gradio	Create customizable UI components around machine learning models
Weights & Biases	Experiment tracking, dataset versioning, and model management
DVC	Open-source version control system for machine learning projects
Optuna	Automatic hyperparameter optimization software framework
Ray Tune	Scalable hyperparameter tuning library
Apache Airflow	Platform to programmatically author, schedule, and monitor workflows
Prefect	Workflow management system for modern data stacks
Kedro	Open-source Python framework for creating reproducible, maintainable data science code
Hamilton	Lightweight library to author and manage reliable data transformations
SHAP	Game theoretic approach to explain the output of any machine learning model
InterpretML	InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations
LIME	Explaining the predictions of any machine learning classifier
flyte	Workflow automation platform for machine learning
dbt	Data build tool
zasper	Supercharged IDE for Data Science
skrub	A Python library to ease preprocessing and feature engineering for tabular machine learning
Codeflash	Ship Blazing-Fast Python Code — Every Time
Hugging Face	Popular open platform for sharing ML models, datasets, and collaborating on NLP and generative AI projects.
Chinese-Elite	An open-source project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph.
Desbordante	An open-source data profiler specifically focused on discovery and validation of complex patterns, such as numerical association rules, differential dependencies, denial constraints, and more.
RunMat	Fast MATLAB-syntax runtime with automatic CPU/GPU execution and fused array kernels.
Turbostream	A terminal UI for experimenting with custom rule engines and selective LLM analysis on real-time data streams, without worrying about streaming infra or backpressure.

Twitter	Description
Big Data Combine	Rapid-fire, live tryouts for data scientists seeking to monetize their models as trading strategies
Big Data Mania	Data Viz Wiz, Data Journalist, Growth Hacker, Author of Data Science for Dummies (2015)
Big Data Science	Big Data, Data Science, Predictive Modeling, Business Analytics, Hadoop, Decision and Operations Research.
Charlie Greenbacker	Director of Data Science at @ExploreAltamira
Chris Said	Data scientist at Twitter
Clare Corthell	Dev, Design, Data Science @mattermark #hackerei
DADI Charles-Abner	#datascientist @Ekimetrics. , #machinelearning #dataviz #DynamicCharts #Hadoop #R #Python #NLP #Bitcoin #dataenthousiast
Data Science Central	Data Science Central is the industry's single resource for Big Data practitioners.
Data Science London	Data Science. Big Data. Data Hacks. Data Junkies. Data Startups. Open Data
Data Science Renee	Documenting my path from SQL Data Analyst pursuing an Engineering Master's Degree to Data Scientist
Data Science Report	Mission is to help guide & advance careers in Data Science & Analytics
Data Science Tips	Tips and Tricks for Data Scientists around the world! #datascience #bigdata
Data Vizzard	DataViz, Security, Military
DataScienceX
deeplearning4j
DJ Patil	White House Data Chief, VP @ RelateIQ.
Domino Data Lab
Drew Conway	Data nerd, hacker, student of conflict.
Emilio Ferrara	#Networks, #MachineLearning and #DataScience. I work on #Social Media. Postdoc at @IndianaUniv
Erin Bartolo	Running with #BigData--enjoying a love/hate relationship with its hype. @iSchoolSU #DataScience Program Mgr.
Greg Reda	Working @ GrubHub about data and pandas
Gregory Piatetsky	KDnuggets President, Analytics/Big Data/Data Mining/Data Science expert, KDD & SIGKDD co-founder, was Chief Scientist at 2 startups, part-time philosopher.
Hadley Wickham	Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.
Hakan Kardas	Data Scientist
Hilary Mason	Data Scientist in Residence at @accel.
Jeff Hammerbacher	ReTweeting about data science
John Myles White	Scientist at Facebook and Julia developer. Author of Machine Learning for Hackers and Bandit Algorithms for Website Optimization. Tweets reflect my views only.
Juan Miguel Lavista	Principal Data Scientist @ Microsoft Data Science Team
Julia Evans	Hacker - Pandas - Data Analyze
Kenneth Cukier	The Economist's Data Editor and co-author of Big Data (http://www.big-data-book.com/).
Kevin Davenport	Organizer of https://www.meetup.com/San-Diego-Data-Science-R-Users-Group/
Kevin Markham	Data science instructor, and founder of Data School
Kim Rees	Interactive data visualization and tools. Data flaneur.
Kirk Borne	DataScientist, PhD Astrophysicist, Top #BigData Influencer.
Linda Regber	Data storyteller, visualizations.
Luis Rei	PhD Student. Programming, Mobile, Web. Artificial Intelligence, Intelligent Robotics Machine Learning, Data Mining, Natural Language Processing, Data Science.
Mark Stevenson	Data Analytics Recruitment Specialist at Salt (@SaltJobs) Analytics - Insight - Big Data - Data science
Matt Harrison	Opinions of full-stack Python guy, author, instructor, currently playing Data Scientist. Occasional fathering, husbanding, organic gardening.
Matthew Russell	Mining the Social Web.
Mert Nuhoğlu	Data Scientist at BizQualify, Developer
Monica Rogati	Data @ Jawbone. Turned data into stories & products at LinkedIn. Text mining, applied machine learning, recommender systems. Ex-gamer, ex-machine coder; namer.
Noah Iliinsky	Visualization & interaction designer. Practical cyclist. Author of vis books: https://www.oreilly.com/pub/au/4419
Paul Miller	Cloud Computing/ Big Data/ Open Data Analyst & Consultant. Writer, Speaker & Moderator. Gigaom Research Analyst.
Peter Skomoroch	Creating intelligent systems to automate tasks & improve decisions. Entrepreneur, ex-Principal Data Scientist @LinkedIn. Machine Learning, ProductRei, Networks
Prash Chan	Solution Architect @ IBM, Master Data Management, Data Quality & Data Governance Blogger. Data Science, Hadoop, Big Data & Cloud.
Quora Data Science	Quora's data science topic
R-Bloggers	Tweet blog posts from the R blogosphere, data science conferences, and (!) open jobs for data scientists.
Rand Hindi
Randy Olson	Computer scientist researching artificial intelligence. Data tinkerer. Community leader for @DataIsBeautiful. #OpenScience advocate.
Recep Erol	Data Science geek @ UALR
Ryan Orban	Data scientist, genetic origamist, hardware aficionado
Sean J. Taylor	Social Scientist. Hacker. Facebook Data Science Team. Keywords: Experiments, Causal Inference, Statistics, Machine Learning, Economics.
Silvia K. Spiva	#DataScience at Cisco
Harsh B. Gupta	Data Scientist at BBVA Compass
Spencer Nelson	Data nerd
Talha Oz	Enjoys ABM, SNA, DM, ML, NLP, HI, Python, Java. Top percentile Kaggler/data scientist
Tasos Skarlatidis	Complex Event Processing, Big Data, Artificial Intelligence and Machine Learning. Passionate about programming and open-source.
Terry Timko	InfoGov; Bigdata; Data as a Service; Data Science; Open, Social & Business Data Convergence
Tony Baer	IT analyst with Ovum covering Big Data & data management with some systems engineering thrown in.
Tony Ojeda	Data Scientist , Author , Entrepreneur. Co-founder @DataCommunityDC. Founder @DistrictDataLab. #DataScience #BigData #DataDC
Vamshi Ambati	Data Science @ PayPal. #NLP, #machinelearning; PhD, Carnegie Mellon alumni (Blog: https://allthingsds.wordpress.com )
Wes McKinney	Pandas (Python Data Analysis library).
WileyEd	Senior Manager - @Seagate Big Data Analytics @McKinsey Alum #BigData + #Analytics Evangelist #Hadoop, #Cloud, #Digital, & #R Enthusiast
WNYC Data News Team	The data news crew at @WNYC. Practicing data-driven journalism, making it visual, and showing our work.
Alexey Grigorev	Data science author
İlker Arslan	Data science author. Shares mostly about Julia programming
INEVITABLE	AI & Data Science Start-up Company based in England, UK
Jan Oliver Rüdiger	ML, DL and Data Science - with a focus on text-/data-mining

Preview	Description
	Key differences of a data scientist vs. data engineer
	A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img)
	Mindmap on required skills (img)
	Swami Chandrasekaran made a Curriculum via Metro map.
	by @kzawadz via [twitter](ht

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AWESOME DATA SCIENCE

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Table of Contents

What is Data Science?

Where do I Start?

Agents

Frameworks

Workflow

Training Resources

Tutorials

Free Courses

MOOC's

Intensive Programs

Colleges

The Data Science Toolbox

Algorithms

Three kinds of Machine Learning Systems

Comparison

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Data Mining Algorithms

Modern Data Mining Algorithms

Deep Learning architectures

General Machine Learning Packages

Deep Learning Packages

PyTorch Ecosystem

TensorFlow Ecosystem

Keras Ecosystem

Visualization Tools

Miscellaneous Tools

Literature and Media

Books

Book Deals (Affiliated)

Journals, Publications and Magazines

Newsletters

Mailing lists

Bloggers

Presentations

Podcasts

YouTube Videos & Channels

Socialize

Facebook Accounts

Twitter Accounts

Telegram Channels

Slack Communities

GitHub Groups

Data Science Competitions

Fun

Infographics

Social Proof

🛡️ Tool Transparency Report

🆔 Identity & Source

⚙️ Technical Specs

📊 Engagement & Metrics