Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
NLP Pre-processing: Tokenization, Stop Words, Lemmatization
Graph Chatbot
Related lectures (32)
Introduction to Data Science
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Recurrent Neural Networks: Training and Challenges
Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.
Deep Learning Techniques: Recurring Networks and LSTM Models
Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.
Mutable and Immutable Objects
Explains the differences between mutable and immutable objects in Python and covers native container types.
Contextual Representations: ELMO and BERT Overview
Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.
Coreference Resolution
Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.
Word Embeddings: Modeling Word Context and Similarity
Covers word embeddings, modeling word context and similarity in a low-dimensional space.
Deep Learning: Principles and Applications
Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Word Embeddings: Introduction and Applications
Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.
Compositional Representations and Systematic Generalization
Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.
Machine Translation: Attention Mechanism
Explores the attention mechanism in machine translation, addressing the bottleneck problem and improving NMT performance significantly.
Introduction to Natural Language Processing
Covers the basics of Natural Language Processing, from traditional to modern approaches, highlighting the challenges and importance of studying both methods.
Previous
Page 2 of 2
Next