Word Embeddings: Word2Vec, GloVe | Mellow Academy
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Word Embeddings: Word2Vec, GloVe

Lesson 6/9 | Study Time: 120 Min
Course: Advanced Topics in Data Science

  • Topics Covered:

    • Understanding Word Embeddings: Introduction to the concept of word embeddings as a way to represent words in a continuous vector space.

    • Word2Vec Model: Deep dive into Word2Vec, explaining skip-gram and continuous bag-of-words (CBOW) models.

    • GloVe Model: Understanding Global Vectors for Word Representation (GloVe) and how it differs from Word2Vec.

    • Using Pre-trained Embeddings: How to leverage pre-trained embeddings (e.g., GloVe, FastText) to improve NLP model performance.

    • Custom Embeddings: Training custom word embeddings on domain-specific corpora.

  • Practical Session:
    • Implementing Word2Vec using Gensim and exploring the embeddings.
    • Using pre-trained GloVe embeddings in a text classification model to improve accuracy.

  • Learning Outcome: Students will gain hands-on experience in creating and using word embeddings, a critical step in improving the performance of NLP models.
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    Class Sessions

    1- Introduction to Neural Networks 2- Convolutional Neural Networks (CNNs) 3- Recurrent Neural Networks (RNNs) and LSTMs 4- Text Preprocessing Techniques 5- Sentiment Analysis 6- Word Embeddings: Word2Vec, GloVe 7- Introduction to Hadoop and Spark 8- Working with HDFS 9- Real-Time Data Processing with Spark Streaming

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