6. Demystifying Recurrent Neural Networks: PyTorch LSTM Implementations
   RNNs are a type of neural network designed to handle sequential data, where the output at each step depends not only on the current input but also on the hidden state from previous steps
7. Taming the Dropout Dragon: Effective Techniques for Disabling Dropout in PyTorch LSTMs (Evaluation Mode)
   Dropout is a technique commonly used in deep learning models to prevent overfitting. It works by randomly dropping out a certain percentage of neurons (units) during training
8. Demystifying Weight Initialization: A Hands-on Approach with PyTorch GRU/LSTM
   GRUs (Gated Recurrent Units) and LSTMs (Long Short-Term Memory) networks are powerful recurrent neural networks (RNNs) used for processing sequential data
9. Unlocking Neural Network Potential: A Guide to Inputs in PyTorch's Embedding, LSTM, and Linear Layers
   The Embedding layer takes integer tensors (LongTensors or IntTensors) as input.These tensors represent indices that point to specific rows in the embedding matrix
10. PyTorch LSTMs: Mastering the Hidden State and Output for Deep Learning
    Deep learning is a subfield of artificial intelligence (AI) that employs artificial neural networks with multiple layers to process complex data
11. Understanding Simple LSTMs in PyTorch: A Neural Network Approach to Sequential Data
    Neural networks are inspired by the structure and function of the human brain.They consist of interconnected layers of artificial neurons (nodes)