**Long Short-Term Memory (LSTM) Overview:**

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data. LSTMs are widely used in tasks involving time series forecasting, natural language processing, and more. They excel at handling sequences with varying lengths and mitigating the vanishing gradient problem.

In this example, we'll use a dataset to demonstrate a simple LSTM for sequence classification.

**Example Using a Dataset:**

**Step 1: Import Libraries**

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import imdb
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Embedding, Dense
from tensorflow.keras.preprocessing.sequence import pad_sequences

**Step 2: Load and Prepare the Dataset**

# Load the IMDb movie reviews dataset
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=10000)

# Pad sequences to ensure equal length
X_train = pad_sequences(X_train, maxlen=100)
X_test = pad_sequences(X_test, maxlen=100)

**Step 3: Create and Train the LSTM Model**

# Create the LSTM model
lstm_model = Sequential([
    Embedding(input_dim=10000, output_dim=32, input_length=100),
    LSTM(128),  # LSTM layer with 128 units
    Dense(1, activation='sigmoid')
])

# Compile the model
lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model on the training data
lstm_model.fit(X_train, y_train, epochs=5, batch_size=64, validation_split=0.2)

**Params That Can Be Changed**
1. **Embedding layer parameters**:
- `input_dim`: Specifies the size of the vocabulary (number of unique words).
- `output_dim`: Defines the dimension of the embedding vectors.

2. **LSTM layer parameters**:
- `units`: Specifies the number of LSTM units (neurons) in the layer.
- `activation`: Specifies the activation function for the layer.

3. **Dense layer parameters**:
- `units`: Specifies the number of neurons in the layer.
- `activation`: Specifies the activation function for the layer.

4. **Compile parameters**:
- `optimizer`: Specifies the optimization algorithm (e.g., 'adam').
- `loss`: Defines the loss function (e.g., 'binary_crossentropy').

**Step 4: Evaluate the Model**

# Evaluate the model on the test data
test_loss, test_accuracy = lstm_model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy: {test_accuracy * 100:.2f}%")

**Explanation:**

1. We import the necessary libraries, including NumPy for numerical operations, Matplotlib for visualization, TensorFlow for deep learning, and more.

2. We load the IMDb movie reviews dataset, which contains movie reviews labeled as positive or negative. We limit the vocabulary size to 10,000 words and pad sequences to ensure they have equal lengths.

3. We create an LSTM model using TensorFlow's Keras API. The model includes an Embedding layer to convert input integers into dense vectors, an LSTM layer with 128 units, and a Dense layer with sigmoid activation for binary classification.

4. The model is compiled with the optimizer and loss function specified. In this case, we use binary cross-entropy as the loss function.

5. The model is trained on the training data for a specified number of epochs and batch size.

6. We evaluate the model's performance on the test data, calculating test accuracy.

LSTMs are powerful for handling sequential data with long-range dependencies. They can be customized by adjusting the number of LSTM units, embedding dimensions, and other hyperparameters to suit specific tasks and datasets.