This tutorial will guide you through the basics of Sequence Models, which are a type of neural network model used in natural language processing, time series analysis, and other sequential data tasks.

Overview

Sequence Models are designed to handle data that is ordered or sequential in nature. They are particularly useful for tasks like language modeling, machine translation, and speech recognition.

Key Concepts

  • RNN (Recurrent Neural Network): A type of neural network where the output from the previous layer is fed as input to the next layer.
  • LSTM (Long Short-Term Memory): An architecture of RNN that helps in capturing long-term dependencies in sequential data.
  • GRU (Gated Recurrent Unit): An alternative to LSTM that is simpler and faster but still effective in capturing long-term dependencies.

Getting Started

To get started with Sequence Models, you will need to have a basic understanding of neural networks and Python programming. You can refer to our Introduction to Neural Networks tutorial for more information.

Implementation

Here is a simple example of a Sequence Model using LSTM in Python:

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(timesteps, features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

Resources

LSTM Architecture


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