📝NLP | Word Embeddings | Word2Vec

In the previous video, we explored the fundamental concepts of word embeddings, and specifically discussed Count-based Word Embedding techniques.

Now, it's time to take a deep dive into the neural network architecture based Word2Vec (Word Embedding Techniques). Join us as we peel back the layers and uncover the inner workings of this groundbreaking approach. You'll learn how Word2Vec leverages neural networks to generate high-quality, context-aware word representations that capture the semantic and syntactic relationships between words.

In this video and next video in the playlist, through detailed explanations and hands-on examples, you'll gain a comprehensive understanding of the feature representation process, the training algorithms (Continuous Bag-of-Words and Skip-Gram), and the optimization techniques that make Word2Vec a game-changer in the world of NLP.

Whether you're a seasoned data scientist or a curious learner, this video will equip you with the knowledge and insights to harness the power of Word2Vec and take your natural language processing projects to new heights.

Timeline of the video:
0:00 Introduction
0:49 What are Word Embeddings?
2:27 Word embedding techniques
4:16 What does Word2Vec technique mean?
5:51 Illustration of Word2Vec
8:08 How the vector values are assigned in Word2Vec (using an example)
10:27 Advantages of Feature Representation
11:10 What is Cosine Similarity?
13:27 Sample Use case: Recommendation Engines
14:44 Concluding Remarks & What Next??

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