Neural Networks for NLPCovers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Convolutional Neural NetworksCovers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Deep Learning for NLPIntroduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Regularized Cross-Entropy RiskExplores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.