📚 node [[layer|layer]]

Welcome! Nobody has contributed anything to 'layer|layer' yet. You can:

Write something in the document below!
- There is at least one public document in every node in the Agora. Whatever you write in it will be integrated and made available for the next visitor to read and edit.
Write to the Agora from social media.
- If you follow Agora bot on a supported platform and include the wikilink [[layer|layer]] in a post, the Agora will link it here and optionally integrate your writing.
Sign up as a full Agora user.
- As a full user you will be able to contribute your personal notes and resources directly to this knowledge commons. Some setup required :)

⥅ related node [[notes meta layer julianlehr]]

⥅ related node [[2003 11 09 vlc media player]]

⥅ related node [[2005 03 08 another business just went massively multiplayer]]

⥅ related node [[2005 11 08 vlc media player]]

⥅ related node [[chip player]]

⥅ related node [[chiptune player]]

⥅ related node [[governance layer]]

⥅ related node [[multiplayer roam]]

⥅ related node [[roam multiplayer]]

⥅ related node [[pace layers]]

⥅ related node [[hidden layers]]

⥅ related node [[calibration_layer]]

⥅ related node [[convolutional_layer]]

⥅ related node [[dense_layer]]

⥅ related node [[fully_connected_layer]]

⥅ related node [[hidden_layer]]

⥅ related node [[input_layer]]

⥅ related node [[layer]]

⥅ related node [[layers_api_(tf layers)]]

⥅ related node [[output_layer]]

⥅ related node [[layer 0]]

⥅ related node [[layer infinity]]

⥅ related node [[massively multiplayer open computing]]

⥅ related node [[mindflayer 1999 animals revenge]]

⥅ related node [[the player of games]]

⥅ related node [[there is a layer of governance infrastructure missing from the web]]

⥅ node [[layer]] pulled by Agora

📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Layer.md by @KGBicheno

layer

Go back to the [[AI Glossary]]

A set of neurons in a neural network that process a set of input features, or the output of those neurons.

Also, an abstraction in TensorFlow. Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function.

⥅ node [[layer-0]] pulled by Agora

📓 garden/neil/layer-0.md by @neil

Layer 0

A total galaxy brain for me just now with regards to [[progressive summarisation]]. I've realised that just by virtue of having 'layer 0', the source text, as part of the progressive summarisation process - I feel less bad about having hundreds of unread articles saved in Wallabag. I feel good about it in fact! It's the first step of note-taking - I know I have source material on the topic to come back to, recommended by someone I trust, when this topic comes back into my focus. I can dig deeper into it then if I want.

This is very positive. I had started to think of the wealth of information out there on the web as [[information overload]]. But now I can go back to thinking about it as an amazing resource, to be tapped into when needed.

See: [[Antilibrary]].

⥅ node [[layer-infinity]] pulled by Agora

📓 garden/neil/layer-infinity.md by @neil

Layer Infinity

In [[progressive summarisation]], you have layers of summary of information.

It starts at [[layer 0]]. I like to think of [[Layer Infinity]] as the Internet as a whole. (Or just all information out there).

⥅ node [[layers_api_(tf-layers)]] pulled by Agora

📓 garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Layers_Api_(Tf.Layers).md by @KGBicheno

Layers API (tf.layers)

Go back to the [[AI Glossary]]

#TensorFlow

A TensorFlow API for constructing a deep neural network as a composition of layers. The Layers API enables you to build different types of layers, such as:

tf.layers.Dense for a fully-connected layer.
tf.layers.Conv2D for a convolutional layer.

When writing a custom Estimator, you compose Layers objects to define the characteristics of all the hidden layers.

The Layers API follows the Keras layers API conventions. That is, aside from a different prefix, all functions in the Layers API have the same names and signatures as their counterparts in the Keras layers API.