I have some data which I would like to analyze using LSTM precisely.

Please indicate if you are experienced or know someone who can do it.

Hydronium:
I have some data which I would like to analyze using LSTM precisely.

Please indicate if you are experienced or know someone who can do it.

Chat me up on 0-8-0-33-05-05-77

Hydronium:
I have some data which I would like to analyze using LSTM precisely.

Please indicate if you are experienced or know someone who can do it.

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The image displays a diagram of a Long Short-Term Memory (LSTM) unit, which is a type of Recurrent Neural Network (RNN) architecture used in deep learning. This LSTM unit is composed of several components that interact with each other to regulate the flow of information. Here's a breakdown of the main elements and their functions:

Cell State (Ct-1, Ct): This is the component that carries information across time steps. The cell state is modified by three gates through element-wise operations, which allow the LSTM to maintain or forget information.

Hidden State (ht-1, ht): Represents the output of the LSTM unit that is passed to the next time step or to a different layer in the neural network. It is also used in the gates' computations.

Input (xt): The current input to the LSTM unit.

Gates: These are mechanisms that regulate the information flow into and out of the cell state and hidden state. Each gate uses a sigmoid activation function (σ) that outputs a value between 0 and 1, representing the extent to which the gate is opened or closed.

Forget Gate (ft): Decides what information to discard from the cell state. It looks at the previous hidden state (ht-1) and the current input (xt), and outputs a number between 0 and 1 for each number in the cell state (Ct-1).

Input Gate (it): Decides which values to update in the cell state.

Output Gate (ot): Determines what the next hidden state (ht) should be.

Candidate for Cell State (C~t): This is a potential addition to the cell state, which is filtered by the input gate.

Pointwise Operations: These are element-wise operations that combine the gate outputs with the cell state. The multiplication by the forget gate output decides what to forget, and the multiplication by the input gate output decides what new information to add.

Updated Cell State: The result of combining the old cell state (after forgetting the designated parts) with the new candidate values (filtered by the input gate).

Updated Hidden State: Determined by the output gate, it is based on the updated cell state and decides the output of the LSTM unit for this time step.

This LSTM unit is designed to handle the problems of vanishing and exploding gradients that occur with standard RNNs by learning what to remember (cell state) and what to output (hidden state) over long sequences, making them highly effective for tasks involving sequential data like time series analysis, natural language

Hydronium:
I have some data which I would like to analyze using LSTM precisely.

Please indicate if you are experienced or know someone who can do it.

hello kindly check your DM