Vinod Codes | AI Engineering & Data Science

NLP by Vinod - Deep Learning Sequence Models RNN for Sequence Modeling - Why Neural Networks Need Memory. After PyTorch and ANN training, I moved to Recurrent Neural Networks to understand why normal ANN and CNN models are not enough when the input is ordered sequential data like text. RNN Sequence Model PyTorch NLP

NLP by Vinod - Deep Learning PyTorch Foundations PyTorch for Deep Learning - From Tensors to ANN Training. After NLP libraries, I moved into deep learning foundations and learned how PyTorch helps build neural networks using tensors, autograd, training loops, DataLoader, GPU training and hyperparameter tuning. PyTorch Tensors Autograd ANN

NLP by Vinod - Foundations NLP Libraries NLP Libraries - NLTK, spaCy, TextBlob and Stanza in Practice. After embeddings, I explored the practical NLP libraries that help with tokenization, POS tagging, NER, sentiment analysis, parsing, multilingual processing and quick NLP experiments. NLTK spaCy TextBlob Stanza

NLP by Vinod - Foundations Text Representation Word Embeddings in NLP - Moving Beyond Sparse Features. After count-based feature extraction, I learned why NLP needs dense vectors that can capture similarity, meaning and context better than Bag of Words and TF-IDF. NLP Embeddings Word2Vec GloVe