First, let's assume that the word vectors have just one dimension instead of 300 dimensions so each word is represented by a single number, such as 100, 14, 17, 10, and 97. Now, let's think about how we'd like to do this with word vectors. The results would be these blue ovals end up in bucket number 1, these gray rectangles end up in bucket number 2, and these magenta triangles are assigned to bucket 3. One bucket can hold more than one item, and each item is always assigned to the same bucket. Let us say you have several data items and you want to group them into buckets by some kind of similarity. In this video, we will dive deeper into these concepts. For example, paper documents alone, keys alone, and books alone. You might want to place similar objects in similar drawers.

For example, imagine that you have a cupboard with several drawers. In this video, you are going to learn about hash tables and hash functions. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. In Course 1 of the Natural Language Processing Specialization, you will:Ī) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes,ī) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, andĬ) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search.īy the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot!

Word documents