This module covers the process of deploying PyTorch models on mobile devices, with a focus on optimizing models for mobile use, integrating camera-based inference, and handling app permissions and deployment on both Android and iOS platforms. Learners will gain practical skills in mobile ML development and cross-platform app integration.

This module focuses on accelerating deep learning model development using PyTorch tools like fastai, PyTorch Lightning, and PyTorch Profiler. Learners will gain practical skills in optimizing model training, analyzing performance during inference, and interpreting model behavior to improve accuracy.

This module introduces learners to the integration of PyTorch and AutoML, focusing on automated neural architecture and hyperparameter search. It covers key tools like Auto-PyTorch and Optuna, helping students streamline model development and optimize performance efficiently.

This module covers techniques for interpreting PyTorch models and understanding their decision-making processes. Learners will explore visualization methods, feature map analysis, and tools like Captum for model explanation. The focus is on making deep learning models more transparent and reliable for real-world applications.

This module guides learners through building a movie recommendation system using PyTorch and the MovieLens dataset. It covers data processing, model training, and embedding techniques used in recommendation systems. Learners will gain practical skills in implementing and training a basic recommendation model.

This module focuses on integrating Hugging Face with PyTorch, exploring key tools like Transformers, Accelerate, and Optimum. Learners will gain hands-on experience with pre-trained models, distributed training, and efficient model deployment workflows.