Introduction Sequence-to-Sequence models transformed deep learning by enabling neural networks to understand one sequence and generate another. Whether it is language translation, text summarization, speech recognition, dialogue systems, caption generation, or code completion Seq2Seq models form the backbone of most…
Introduction Recurrent Neural Networks (RNNs) allow models to learn from sequential data text, speech, sensor signals, time-series patterns, and more. But as sequences grow longer, traditional RNNs struggle with vanishing gradients, losing information from earlier time steps. This limitation led…
Introduction Recurrent Neural Networks (RNNs) struggle with long-term memory because gradients either explode or vanish during backpropagation. LSTM Networks were designed to fix this weakness, making them one of the most important architectures in modern deep learning. In this lecture,…
Recurrent Neural Networks (RNNs) are, how they handle sequence data, their internal architecture, types (Simple RNN, LSTM, GRU), training challenges, and real-world applications such as NLP, speech recognition, and time-series forecasting. Introduction to RNNs Recurrent Neural Networks (RNNs) are a…
Convolution operations form the core engine of Convolutional Neural Networks (CNNs). Every powerful deep learning model from VGG and ResNet to YOLO and EfficientNet relies on convolution to extract meaningful patterns from images. A convolution layer identifies edges, textures, colors,…
Convolutional Neural Networks (CNNs) rely heavily on two structural controls that shape how images move through the network: padding and stride. Without these two settings, CNNs would lose important spatial information and fail to extract features effectively.In this lecture, we…