Deep Learning | Video 4 | Part 1 | Recurrent Neural Networks (RNN) & LSTMs | Venkat Reddy AI Classes

Course Materials https://github.com/venkatareddykonasani/Youtube_videos_Material To keep up with the latest updates, join our WhatsApp community: https://chat.whatsapp.com/GidY7xFaFtkJg5OqN2X52k In this video, we delve into Recurrent Neural Networks (RNNs) and a specific variation known as Long Short-Term Memory (LSTM). We explore the basics of RNNs, their applications in daily life, and the challenges they address. We start by discussing how RNNs are used to predict the next item in a sequence of data, like word prediction in text messaging or forecasting stock prices based on historical data. Unlike other models such as Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), RNNs excel in handling sequential data where the order is crucial. ANNs, while powerful, fail with sequential data because they don't preserve the sequence of input data. CNNs, designed for images, lack the ability to retain the exact order of words in a sequence. To address this, we introduce the concept of sequential ANNs, which adapt traditional ANN models to work with sequential data. We explain the solution: building sequential ANN models where each model predicts the next item in the sequence based on the previous one. By connecting these models and passing along information between them, we ensure that the sequence's context is preserved, making RNNs suitable for sequential data. This video breaks down the technical details step-by-step, demonstrating how to build and connect sequential ANN models to solve real-world problems with sequential data effectively. Join us as we uncover the world of Recurrent Neural Networks and understand how they power applications we use every day! For more informative videos on AI, Machine Learning, and Neural Networks, don't forget to subscribe to our channel! #AI #MachineLearning #DeepLearning #NeuralNetworks #ai #RNN #LSTM #DataScience #ArtificialIntelligence #Tech #genai #promptengineering