Drawbacks Of One-hot Encoding | Introduction To Word Embeddings | Load And Use Pretrained Word2Vec
Drawbacks Of One-hot Encoding | Introduction To Word Embeddings | Load And Use Pretrained Word2Vec
************************************
This video of deep learning's coading tutorial, explains problems with one hot encoding. One hot encoding suffers from sparseness, bulky size, binary nature and meaningless formation of vector. These disadvantages are removed by word embeddings. In this video, we have explained advantages of word embeddings over one hot encoding. Word embeddings are real valued vector with smaller size. These vectors are considering semantics and meaning of word. We have demonstrated word embedding with popular Word2Vec model. We have also explained two flavors of Word2Vec that Continuous Bag of Vectors and Skip Gram Models. We have loaded pretrained word embeddings for words given by pretrained Word2Vec model over large Google News Corpus. We have also demonstration vector operations and similarity search on different word. In this video we have worked on following questions:
What is hot encoding in NLP?
What is the drawback of using one hot encoding?
What is word embedding in deep learning?
What is word embedding used for?
What does embedding mean?
What does word embedding mean?
We have downloaded pre-trained word embeddings from official link: https://drive.google.com/file/d/0B7Xk...
********************************************
#Pathshala #DLPathshala #Word2Vec #IntroductionToWordEmbeddings #WordEmbeddings #DrawbacksOfOneHotEncoding #OneHotEncoding