Nowadays, LSTMs have been replaced by the[Transformer](https://jalammar.github.io/illustrated-transformer/)( which uses[Attention](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) to tackle all the problems that plague an LSTM such as as lack of[transfer learning](https://en.wikipedia.org/wiki/Transfer_learning), lack of[parallel training](https://web.stanford.edu/~rezab/classes/cme323/S16/projects_reports/hedge_usmani.pdf) and a long gradient chain for lengthy sequences

Building a neural network from scratch with NumPy is a great way to learn more about NumPy and about deep learning. However, for real-world applications you should use specialized frameworks — such as PyTorch, JAX, TensorFloworMXNet — that provide NumPy-like APIs, have built-in automatic differentiation and GPU support, and are designed for high-performance numerical computing and machine learning.

Building a neural network from scratch with NumPy is a great way to learn more about NumPy and about deep learning. However, for real-world applications you should use specialized frameworks — such as PyTorch, JAXorTensorFlow — that provide NumPy-like APIs, have built-in automatic differentiation and GPU support, and are designed for high-performance numerical computing and machine learning.

Finally, to know more about how ethics come into play when developing a machine learning model, you can refer to the following resources :

- Data ethics resources by the Turing Institute.https://www.turing.ac.uk/research/data-ethics