Build your Own Word2Vec Model | NLP Learn from Scratch series

Miro Notes: https://miro.com/app/board/uXjVIGU4trY=/?share_link_id=612221737460 Follow Sharvesh Subhash for more updates: https://www.linkedin.com/in/sharveshsubhash/ --------------------------------------------------------------------------------------- Notebook link in the end. --------------------------------------------------------------------------------------- Timestamps 00:00 - Introduction & Recap of Text Representations 01:29 - CBOW vs Skip-Gram Architecture Overview 04:00 - Understanding One-Hot Encoding & Embedding Matrix 08:59 - Mathematical Derivation of Word2Vec Architecture 14:10 - Data Preprocessing & Context Window Creation 18:22 - Aggregating Context Vectors & Hidden Representation 21:05 - Output Layer, Softmax & Cross Entropy Loss 23:06 - Weight Updates via Gradient Descent 24:05 - Word2Vec Pipeline Architecture Summary 29:14 - Data Loading & Vocabulary Preparation 32:32 - CBOW Dataset Class Creation 34:49 - Model Architecture Using `nn.Embedding` 36:12 - Training the Word2Vec Model in PyTorch 39:38 - Extracting Learned Word Embeddings 40:14 - Cosine Similarity on Custom Embeddings 41:14 - Conclusion & What's Next In this comprehensive lecture, we take a deep dive into Word2Vec—one of the most influential models for learning word representations in Natural Language Processing. You'll not only learn the theoretical foundations behind Word2Vec but also build and train your own Word2Vec model from scratch using Python and PyTorch! 🧠 What You'll Learn: Quick recap of symbolic & distributed representations (One-Hot, BoW, TF-IDF, etc.) Introduction to word embeddings and pretrained models like GloVe and FastText Deep dive into Word2Vec architectures: CBOW (Continuous Bag of Words) & Skip-Gram Model Mathematical derivation of embedding and output layers Training mechanics: one-hot encoding, embedding matrix, hidden layer, softmax, and loss computation. Detailed explanation of context window, aggregation, backpropagation, and optimization. Full implementation walkthrough using WikiText dataset. Visual explanation of architecture and training flow.Compute cosine similarities using your own trained embeddings. 💻 Hands-On Code Implementation: You'll build a custom Word2Vec model in PyTorch, using: nn.Embedding layers Custom dataset class for CBOW Training loop with cross-entropy loss Cosine similarity function using your own trained vectors --------------------------------------------------------------------------------------------- 📓 Notebook Link: https://colab.research.google.com/drive/13ApfVWPOlXZagZzS_FOXZ-f4JXOEIlIl?usp=sharing Recommended for: Aspiring NLP engineers, AI students, and researchers who want to understand and implement Word2Vec. #naturallanguageprocessing #datascientist #AIReserachers #NLPexperts #nlpenthusiaists #nlp #practicalnlp #machinelearning #machinelearningfornlp #deeplearning #word2vec #vectorization