Deep learning: A Natural Language Processing Bootcamp

Explore natural language processing by tackling tokenization, verdant buildings, recurrent neural networks and their types such as DMs, and sequence modules to build a model that can write like Shakespeare.

Explore the structure of neural networks, including input and hidden layers, weights, biases, and activation functions like relu and sigmoid, plus basics of learning, classification, and regularization to prevent overfitting.

Explore Google Colab as a free cloud platform with preinstalled libraries for Python and deep learning, enabling notebook work, code execution, and faster model training with internet and Google account.

Explore how tokenization works in natural language processing by examining how punctuation is handled, how words are indexed by frequency, and how a vocabulary is built without separate punctuation tokens.

Explore how out-of-vocabulary words are handled in tokenization by assigning an OOV token, replacing unseen words in sentences, and enabling identification of unknown terms.

The lecture explains using an out-of-vocabulary (oov) token by introducing a betokens parameter, builds a vocabulary from sample sentences, and assigns tokens to unknown words during sequence processing.

Learn how padding standardizes sentence lengths in NLP by adding zeros to token sequences using a text to sequence function, to align inputs and enable flexible parameter control.

Explore padding in text sequences for deep learning, including tokenization, vocabulary, and max length. Compare front and back zero padding and see how fixed-length inputs affect model training.

Design and complete the class project by downloading and editing the sarcasm dataset, building a vocabulary, performing predefined tasks, and tokenizing and sharing a sample sentence with the class.