This course introduces Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), focusing on their applications in sequential data analysis and time series prediction. Students will learn how to implement and train LSTM models using Python and popular deep learning frameworks.
Welcome to our comprehensive course on Deep LSTM (Long Short-Term Memory) networks! Dive into the world of deep learning with a focus on LSTM architecture, its significance and practical applications. Beginning with an introduction to Deep LSTM networks, we explore their architectural intricacies, including input, hidden and output layers, along with neuron configurations. 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.