NLP Pre-processing: Tokenization, Stop Words, Lemmatization

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Page 3 of 4

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Explains the differences between mutable and immutable objects in Python and covers native container types.

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.