Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.
Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.
