What’s the difference of stateless LSTM and a normal feed-forward NN?

Answer: Stateless LSTM disregards past sequences, while a feed-forward neural network lacks internal memory and processes inputs sequentially.

Here is a comparison between stateless LSTM and a normal feed-forward NN:

Aspect	Stateless LSTM	Feed-Forward Neural Network (NN)
Architecture	Incorporates Long Short-Term Memory (LSTM) cells that allow capturing temporal dependencies and sequence information.	Consists of layers where information flows in one direction without internal memory or consideration of temporal dependencies.
Memory Handling	Stateless; each input is treated independently without maintaining the memory of past sequences.	No internal memory; processes inputs sequentially without considering the past or future context.
Temporal Dependencies	Suited for tasks with sequential or time-dependent patterns where understanding past context is essential.	Generally less effective for tasks with sequential dependencies, as they lack explicit memory of past inputs.
Use Cases	Time-series prediction, natural language processing tasks, and other applications with sequential data.	Image classification, static pattern recognition, and tasks where temporal order is less crucial.
Training Complexity	May require additional considerations for sequence handling, and vanishing/exploding gradient issues are common in training.	Typically simpler to train as they don’t involve handling sequential dependencies, making convergence more straightforward.
Computational Resources	Tends to be more computationally intensive due to the nature of recurrent connections and memory cells.	Generally less computationally demanding, making them suitable for scenarios with resource constraints.
Interpretability	Provides a certain level of interpretability through analyzing sequential patterns in data.	Generally less interpretable due to the lack of explicit consideration for temporal relationships.
Overfitting Risk	Prone to overfitting on small datasets, and regularization techniques are often needed.	May be less prone to overfitting in simpler tasks, but regularization is still relevant in complex architectures.

Conclusion:

In summary, a stateless LSTM is designed for tasks involving sequential data and temporal dependencies, leveraging memory cells to capture patterns over time. On the other hand, a feed-forward neural network lacks explicit memory and is suitable for tasks where temporal order is less critical, making it computationally more efficient but potentially less effective for sequence-related tasks.