At a Glance

Name: Master Word Embeddings from Scratch with Word2Vec & PyTorch
Price: Free CAD
Rating: 4.0 (1 reviews)
Author: kunal_makwana, fateme_akbari, karan_goswami

Learn to create word embeddings from scratch using Word2Vec and PyTorch. In this project, you'll implement Continuous Bag of Words (CBOW) and Skip-gram models, essential for Natural Language Processing (NLP) tasks. Gain a deep understanding of how word embeddings represent text data, enabling better context and meaning extraction. This hands-on project focuses on building foundational skills in NLP, empowering you to better understand and apply word embedding techniques to real-world text-processing tasks.

Unlock the power of Word2Vec for natural language processing in this hands-on project. Learn to implement and train Continuous Bag of Words (CBOW) and Skip-gram models using PyTorch, mastering how word embeddings represent relationships between words. Additionally, explore GloVe (optional but fun to learn) as another powerful technique for generating word embeddings, broadening your understanding of NLP. With practical coding exercises, this project helps you understand the principles behind Word2Vec and introduces GloVe, foundational NLP techniques used in search engines, recommendation systems, and sentiment analysis.

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Why This Topic Is Important:

Word embeddings are crucial for understanding and processing text data in real-world applications, from chatbots to text classification systems. This project offers a clear and practical introduction to Word2Vec while also introducing GloVe, giving you insights into two widely used techniques in NLP workflows. By completing this project, you’ll gain the foundational knowledge to use word embeddings effectively and enhance your ability to tackle language-driven challenges in AI and machine learning.

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A Look at the Project Ahead:

In this project, you’ll implement Word2Vec models using PyTorch and understand the mechanics of CBOW and Skip-gram architectures. You’ll also have the opportunity to explore GloVe as an optional but valuable technique for generating word embeddings. Dive into the mathematical foundations and coding implementation of word embeddings while exploring their applications in NLP.

By the end of the project, you will:

Understand the principles of Word2Vec and the role of CBOW and Skip-gram models in generating word embeddings.
Learn to train Word2Vec models from scratch using PyTorch, equipping you with hands-on experience in building and testing embeddings.
Explore how word embeddings capture semantic relationships between words for improved text representation in machine learning tasks.

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What You’ll Need

To successfully complete this project, you’ll need:

A basic understanding of Python programming and experience with PyTorch.
Familiarity with foundational concepts in natural language processing.
A web browser to access tools and execute code.

This project is perfect for NLP enthusiasts and professionals looking to deepen their understanding of word embeddings while enhancing their machine learning skills.

Offered By: IBMSkillsNetwork

Master Word Embeddings from Scratch with Word2Vec & PyTorch

At a Glance

Why This Topic Is Important:

A Look at the Project Ahead:

What You’ll Need