Solved Deep Learning MCQs, short questions, and long questions for BSCS, NCEAC. Covers CNN, RNN, LSTM, GANs, Transformers, Autoencoders, and Optimization algorithms. Perfect for university exams. SECTION A – MCQs (WITH ANSWERS + EXPLANATIONS) 1. Deep Learning is mainly inspired…
Introduction Unsupervised Deep Learning plays a critical role in extracting representations from raw data without labels. While supervised networks learn mappings from input to output, unsupervised networks learn the underlying structure and distribution of the data itself. This enables neural…
Introduction Transformers are the most powerful and game-changing architecture in modern deep learning. From BERT and GPT-4 to Stable Diffusion and Gemini nearly every cutting-edge AI model today is built using the Transformer architecture. Before Transformers, RNNs, LSTMs, and GRUs…
Introduction Attention mechanisms dramatically changed the trajectory of deep learning. Before attention, models like RNNs, GRUs, and LSTMs struggled with long-term dependencies, losing information from early parts of a sequence. Attention solved this by giving models the ability to focus…
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…