Udemy course Word2Vec: Build Semantic Recommender System with TensorFlow by GoTrained Academy
Word2Vec: Build Semantic Recommender System with TensorFlow is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Word2Vec: Build Semantic Recommender System with TensorFlow udemy course.
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Course data:
- Author: GoTrained Academy
- Course rating: 3.5
- Category: Development
- Modality: Online
- Status: Available
- Idiom: English
Abouth GoTrained Academy
GoTrained is an e-learning academy aiming at creating useful content in different languages and it concentrates on technology and management.
What the udemy Word2Vec: Build Semantic Recommender System with TensorFlow course teaches?
What you’ll learn Building and Training a Word2vec Model with Python TensorFlow Semantic Recommender System – Practical Project to Semantically Suggest Names Source Code *.py Files of All Lectures English Captions for All Lectures Q&A board to send your questions and get them answered quickly
Word2Vec Tutorial: Names Semantic Recommendation System by Building and Training a Word2vec Python Model with TensorFlow
More information about the course Word2Vec: Build Semantic Recommender System with TensorFlow
In this Word2Vec tutorial , you will learn how to train a Word2Vec Python model and use it to semantically suggest names based on one or even two given names . This Word2Vec tutorial is meant to highlight the interesting, substantive parts of building a word2vec Python model with TensorFlow . Word2vec is a group of related models that are used to produce Word Embeddings . Embedding vectors created using the Word2vec algorithm have many advantages compared to earlier algorithms such as latent semantic analysis. Word embedding is one of the most popular representation of document vocabulary. It is capable of capturing context of a word in a document, semantic and syntactic similarity, relation with other words, etc. Word Embeddings are vector representations of a particular word. The best way to understand an algorithm is to implement it. So, in this course you will learn Word Embeddings by implementing it in the Python library, TensorFlow . Word2Vec is one of the most popular techniques to learn word embeddings using shallow neural network . Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. In this Word2Vec tutorial, you will learn The idea behind Word2Vec: Take a 3 layer neural network. (1 input layer + 1 hidden layer + 1 output layer) Feed it a word and train it to predict its neighbouring word. Remove the last (output layer) and keep the input and hidden layer. Now, input a word from within the vocabulary. The output given at the hidden layer is the ‘word embedding’ of the input word. In this Word2Vec tutorial we are going to do all steps of building and training a Word2vec Python model (including pre-processing, tokenizing, batching, structuring the Word2Vec Python model and of course training it) using Python TensorFlow. Finally, we are going to use our trained Word2Vec Python model to semantically suggest names based on one or even two given names. Let’s start!
