A Guide to Four Deep Learning Layers

A Guide to Four Deep Learning Layers

This post is about four important neural network layer architectures — the building blocks that machine learning engineers use to construct deep learning models. Fully connected, convolution, LSTM & attention all illustrated and explained.

Fully connected, convolution, LSTM & attention all illustrated and explained.

This post is about four important neural network layer architectures — the building blocks that machine learning engineers use to construct deep learning models:

  1. fully connected layer,
  2. 2D convolutional layer,
  3. LSTM layer,
  4. attention layer.

For each layer we will look at:

  • how each layer works,
  • the intuition behind each layer,
  • the inductive bias of each layer,
  • what the important hyperparameters are for each layer,
  • when to use each layer,
  • how to code each layer in TensorFlow 2.0.

All code examples are built using tensorflow==2.2.0 using the Keras Functional API.

data-science artificial-intelligence tensorflow deep-dives machine-learning

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