#Neural networks

PyTorch Forecasting uses PyTorch's deep learning architectures to simplify time series forecasting for professionals and beginners. Built on PyTorch Lightning, it supports training on both CPUs and GPUs. Key features include a versatile time series dataset class, a robust model training framework with visualization tools, and various neural network architectures optimized for real-world applications. Featuring multi-horizon metrics and hyperparameter tuning using Optuna, this package supports accurate predictions in diverse scenarios. Explore a range of tutorials and resources to enhance forecasting capabilities.

Augraphy provides a Python-based augmentation pipeline simulating realistic document distortions for AI/ML training. Its customizable features assist in creating varied synthetic data crucial for tasks like OCR and document classification.

Utilizing neural ray-surface distance fields, this method enables 1000x faster 3D rendering with multi-view consistency. Tested on Blender, DM-SR, and ScanNet for reliable, high-quality depth images across varied settings.

Discover detailed resources from the Fall 2023 Deep Learning course. Access weekly lecture and practice materials that can be completed locally or through Google Colab. Stay connected via the Telegram chat for community engagement and refer to the online page for deadlines and grading information. The course covers a range of topics including neural networks, convolutional networks, and adaptive optimization. Materials are developed by experts like Victor Lempitsky, Victor Yurchenko, and others, providing content on generative models and PyTorch fundamentals. Engage with the course through collaborative insights and continuous enhancements.