Скачать с ютуб Distributed Representations: Pretrained Word Embeddings & t-SNE| NLP from Scratch series| Module 8 в хорошем качестве

Distributed Representations: Pretrained Word Embeddings & t-SNE| NLP from Scratch series| Module 8

Miro Notes: https://miro.com/app/board/uXjVICLIG6... --------------------------------------------- Follow Sharvesh Subhash on Linkedin for more updates:   / sharveshsubhash   Colab notebook link in the end. -------------------------------------------- Timestamps 00:05 - Introduction to Distributed Representations 00:21 - Recap of Basic Vectorization Approaches 01:05 - Techniques in Basic Vectorization: One-Hot Encoding, Bag of Words, Bag of N-Grams, and TF-IDF 01:22 - Limitations of Basic Vectorization (Discrete Symbols, High Dimensionality, Sparsity) 04:18 - Transition to Distributed Representations 05:09 - Discrete vs. Distributional Representations 05:49 - Overview of Distributed Representations (Low-Dimensional Dense Vectors) 06:06 - Distributional Similarity Explained with Examples 07:20 - Distributional Hypothesis and Contextual Similarity 09:21 - Distributional Representation Techniques (Bag of N-Grams, TF-IDF) 12:21 - Challenges of Distributional Representations (High Dimensionality and Sparsity) 13:17 - Introduction to Distributed Representations (Compact Dense Vectors) 14:19 - Using Neural Networks for Distributed Representations 15:32 - Visualizing Word Clusters in Vector Space (Similarity in Distributed Representations) 15:52 - Explanation of Embeddings and Their Role in NLP Welcome to the "Natural Language Processing Learn from Scratch" lecture series! This video focuses on the concept of distributed representations in NLP. It begins with a recap of basic text vectorization techniques such as one-hot encoding, bag of words, bag of n-grams, and TF-IDF, highlighting their limitations including discrete symbol representation, high dimensionality, sparsity, and inability to handle out-of-vocabulary words. The lecture then introduces distributed representations as a solution, explaining how neural networks are used to generate dense, low-dimensional embeddings that capture semantic relationships between words. Key concepts such as distributional similarity and the distributional hypothesis are discussed, emphasizing that words appearing in similar contexts tend to have similar meanings. The video contrasts distributional representations (which use high-dimensional sparse vectors based on co-occurrence statistics) with distributed representations (which produce compact, dense vectors). Examples illustrate how distributed word embeddings cluster semantically similar words together in vector space, improving computational efficiency and capturing nuanced meanings. The video also explains the term "embedding" as the transformation of text into numerical vectors that represent words or phrases in a meaningful way. Overall, this lecture provides a comprehensive introduction to the evolution from basic vectorization methods to advanced distributed representations in NLP, laying the foundation for understanding word embeddings and their importance in modern language models. --------------------------------------------------------------------------------- Colab Notebook link: https://colab.research.google.com/dri... #nlp #wordembeddings #practicalnlp #learnfromscratch #naturallanguageprocessing #machinelearningfornlp #machinelearning #deeplearningfornlp #deeplearning #tsne #handsonlearning #artificialintelligence #datascience #dataanalysis #datavisualisation #embeddings #languageprocessing #visualizingdata #visualizing