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…
Pooling layers are a core component of Convolutional Neural Networks (CNNs). After convolutions extract features, pooling layers compress and refine those features, making the network faster, more stable, and more robust. Without pooling, CNNs would be slow, memory-heavy, and overly…
Introduction Convolutional Neural Networks (CNNs) are the foundation of modern computer vision and one of the most important architectures in deep learning. CNNs excel at understanding images by automatically detecting edges, textures, shapes, and full objects, making them ideal for…