### What is Long-Short Term Memory (LSTM)?

LSTM is an enhancement used to improve the performance of a Recurrent Neural Network

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LSTM is an enhancement used to improve the performance of a Recurrent Neural Network

The Recurrent Neural Network (RNN) is designed to work with data that naturally exists as part of a sequence

In Neural Networks, a batch is a subset of the training data that the network sees before the parameters are updated.

Dropout refers to randomly turning off hidden units so that a smaller network is trained on a given pass

One of the main drawbacks of deep learning is that it is more prone to overfitting

The following are some options that have been shown to reduce the risk of experiencing a vanishing or exploding gradient

The vanishing or exploding gradient is an issue often encountered in the training of deep Neural Networks.

In a deep network with many hidden layers, it can be very computationally intensive to compute derivatives of all of the parameters of the network.

In Backwards Propagation, the parameters of a Neural Network (all of the weight and bias terms) are updated using a gradient descent optimization

Multilayer perceptrons models are suitable for both regression and classification tasks

Find out all the ways

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)