Advanced PyTorch Techniques and Applications

Comprehensive introduction to recommender systems, covering foundational concepts, dataset creation, model development, training, and evaluation metrics including precision and recall.

Deep dive into autoencoder architecture, theory, and practical implementation, focusing on dimensionality reduction and data compression applications.

Exploration of GAN principles, implementation, and practical applications, including hands-on coding exercises for developing GAN models.

Comprehensive coverage of GNNs, including data preparation, model training, and evaluation for node classification tasks.

Introduction to Transformer architecture and Vision Transformers (ViT), including implementation and training on custom datasets.

Overview of PyTorch Lightning framework, including model implementation and early stopping techniques for optimized training.

Exploration of semi-supervised learning techniques, including supervised reference models and methods for utilizing both labeled and unlabeled data.

Comprehensive coverage of NLP techniques, including word embeddings, sentiment analysis, pre-trained models, zero-shot classification, and vector databases.

Coverage of various ML topics including ResNet, Inception architecture, and Extreme Learning Machines, with theoretical and practical implementations.

Focus on model debugging techniques using hooks, including theoretical understanding and practical implementation.

Comprehensive coverage of model deployment strategies, including Flask implementation, API development, and Google Cloud deployment.

Course conclusion summarizing key concepts and providing resources for further learning in advanced PyTorch techniques.