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Journey Through Embeddings: CBOW with negative sampling

In previous post, we explored the fundamentals of Word2Vec and implemented the Continuous Bag of Words (CBOW) model, which efficiently predicts target words based on their surrounding context. While CBOW is powerful, scaling it to large vocabularies can be computationally challenging due to the softmax function. This is where negative sampling comes in—a clever optimization […]

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Journey Through Embeddings: Word2vec CBOW

Word embeddings have revolutionized natural language processing by transforming text into meaningful numerical representations that capture the relationships between words. In this blog, we focus on Word2Vec, a foundational technique in embedding methods, with a particular emphasis on the Continuous Bag of Words (CBOW) model. CBOW learns embeddings by predicting a target word from its

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Clustering with Union-Find: Single-Linkage Implementation

In this post, we’ll explore how the union-find data structure functions and its importance in machine learning applications. We’ll specifically focus on its role in clustering algorithms, demonstrating how it helps manage dynamic cluster formation and identify connected components efficiently. By implementing hierarchical clustering, you’ll see firsthand how this structure enhances the performance of algorithms,

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Mastering Backpropagation: Math and Implementation

Artificial intelligence (AI) has rapidly advanced, transforming industries and pushing technological boundaries. Central to this progress is backpropagation, the essential algorithm that enables neural networks to learn and adapt. Often considered the heartbeat of AI, backpropagation drives the learning process, allowing machines to improve with experience. In this blog post, we’ll demystify backpropagation, exploring its

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