LSTM in Deep Learning

We start off by providing an overview of deep LSTM networks and then delve into their structural complexities, encompassing input, hidden, and output layers, as well as neuron arrangements. Weight initialization techniques and essential hyperparameters such as epochs and learning rates are covered in detail. You’ll gain insights into various activation and loss functions crucial for LSTM networks, alongside training methodologies like Gradient Descent, Adam, and Stochastic Gradient Descent with Momentum. Practical sessions include data explanation, numerical examples, and implementation in both MATLAB and Python, ensuring a holistic understanding of Deep LSTM networks for real-world deployment.