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The Atlas of Human Knowledge

By @frbbdnforest

Atlas of Human Knowledge is a collection of knowledge maps computed by humans and algorithms.

Join the community for info to contribute to the project and to share or trade your expertise.
Help others in knowledge discovery and anticipate the core aspects they will have to deal with in their own field of study, to make sense of new competencies, technologies and concepts.

Books and news will be automatically aggregated for each topic. You can contribute by explaining an argument and its context with your own videos, data-insights or articles. You can also uncover relationships related to key organisations, people, personalities, expertises, or specific to an industrial domain.

Each map will be automatically linked with a "connectome" of human knowledge - like new synapses in a collective brain.
You can navigate collective knowledge, from your point of view or expertise, and always interact with the context of each entity, by expanding relationships that other People or Algorithms contributed to find.

#Fast.Ai #Deeplearning #Fitting.Json

About fast.ai,

Deep-learning, Fitting: a conceptual map of the topics you should become familiar with, when approaching application for machine learning.
This map addresses new beez and novices, and can be used as a visual reference of concepts in linear algebra, tech applications and core algorithms you will meet in a training course on Deep Learning.
Before diving into details, familiarise with words and topics you will hear for a first time.
For your reference: Fast.AI - Lesson 1.

Context of fast.ai, deeplearning and fitting in 62 topics.jsonclose

About fast.ai, Deep-learning, Fitting: a conceptual map of the topics you should become familiar with, when approaching application for machine learning.
This map addresses new beez and novices, and can be used as a visual reference of concepts in linear algebra, tech applications and core algorithms you will meet in a training course on Deep Learning.
Before diving into details, familiarise with words and topics you will hear for a first time.
For your reference: Fast.AI - Lesson 1.

View map
#Stablecoin #SmartContract #Tokenization

This map introduces to what stable coins are: a crypto coin but anchored to "real money" (fiat coins).

The map displays connections to normative context in Europe and opportunities of business models that may make use of tokenisation and stable coins - such as mobile phone money transfers.

Context of stablecoin, smart contract and tokenization in 45 topicsclose

This map introduces to what stable coins are: a crypto coin but anchored to "real money" (fiat coins). The map displays connections to normative context in Europe and opportunities of business models that may make use of tokenisation and stable coins - such as mobile phone money transfers.

View map
#MachineLearning #SupervisedLearning #UnsupervisedLearning

This map shows learning paths to introduce to the subject of machine learning.

It highlights the context of key areas, as supervised, unsupervised, batch learning and topics of data science. As a reference, see the book by Aurélien Géron - Hands on Machine Learning.

Context of machine learning; supervised learning and unsupervised learning in 58 topicsclose

This map shows learning paths to introduce to the subject of machine learning. It highlights the context of key areas, as supervised, unsupervised, batch learning and topics of data science. As a reference, see the book by Aurélien Géron - Hands on Machine Learning.

View map
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